Keywords: Power Systems and Power Electronics, Control Applications, Large-scale Systems
Abstract: In this paper, we propose a novel method for designing PnP(plug-and-play)-type modules of photovoltaic(PV) generators. The proposed module comprises a PV and a compensator that controls the duty cycle of the inverter cancelling out the negative influence of the newly added PV on the entire power system. The design and implementation of the compensator only depend on the local information about the power-system component that is connected to the PV. Even though the proposed module can be constructed locally, the stability of the entire system integrated with the module is theoretically guaranteed. We demonstrate the effectiveness of the proposed method through an example of a nine-bus power system.

Keywords: Power Systems and Power Electronics, Identification and Estimation
Abstract: In this paper, we present a remodeling scheme of so-called RC (Resistance–Capacitance) circuit building thermodynamics models with solar radiation. We first build a 3-D building structure model of a real building in Tokyo Institute of Technology, a detailed EnergyPlus simulator associated with the structure model, and its RC circuit model identified by BRCM (Building Resistance-Capacitance Modeling) MATLAB toolbox. It is then confirmed that the RC circuit model does not always fit the temperature trajectories by EnergyPlus in the presence of solar radiation, while it works well in the absence of radiation. This paper is thus intended to reidentify the solar absorption coefficients, which quantifies the effects of solar radiation on zone temperatures, through data generated by EnergyPlus. We first apply the standard least square method and point out two difficulties: (i) the rank of the data matrix gets ill-conditioned, and (ii) increasing the volume of the data is prohibited due to high dimensionality of the RC circuit model. We thus employ a regularization technique without increasing the computational efforts and validate the resulting model through comparisons with the EnergyPlus data.

Keywords: Power Systems and Power Electronics, Optimal Control and Optimization
Abstract: In this paper, load frequency control problem for power system is discussed. In the proposed method, we consider power system model including generator and battery. Also, saturation is taken into account as a constraint on the input to the model for generator and battery. Then, a cost function considering the storage capacity constraint is formulated. The connected power system cooperatively determines control input that minimize the cost function and keep the power system stable. Furthermore, we propose to consider the economic dispatch methodology considering the suppression of the fuel cost in power system. Therefore, load frequency control and economic dispatch methodology considering constraints of generator and battery are proposed in this paper. Finally, we confirm the effectiveness of our proposed method through numerical simulation.

Keywords: Networked Control Systems, Optimal Control and Optimization
Abstract: This paper is concerned with distributed multi-agent optimization with inequality and equality constraints based on exact penalty methods. In the literature of exact penalty methods, constrained optimization problem is solved through optimization of penalized objective function and, under mild assumptions, the set of the optimal solutions of the penalized objective function coincides with that of the original constrained optimization problem. Exploiting the exactness of the penalized objective function, this paper proposes a new distributed multi-agent optimization protocol which simplifies the update law of previous protocols based on exact penalty methods with equality constraints. Also more concrete proof of the convergence of the protocol is provided under the assumption of the exactness of the penalized objective function.

Keywords: Power Systems and Power Electronics, Decentralized Control, Optimal Control and Optimization
Abstract: In this paper, we analyze the stability of the electricity market trading system with communication delays based on Passivity-Short and show the electricity market trading system has the characteristics of plug and play operation. And then, we propose the dynamic pricing method considering negative price when supply surplus occurs. Regarding this system, especially, even when there is a power flow among each area and communication delays among market participants, we show that the stability of market trading system is guaranteed. Finally, the effectiveness of the proposed method is confirmed by numerical simulations.

Keywords: Power Systems and Power Electronics, Optimal Control and Optimization, Large-scale Systems
Abstract: This paper addresses a novel incentive and control mechanism among a principal and multiple agents with long-term average reward in stochastically dynamical systems. In actual physical systems such as dynamic power systems, the principal called a system operator cannot directly operate the agents' control input in real time to guarantee the agents' voluntary decision makings. Meanwhile, the operations maximizing the agents' own profit is not always to achieve the social welfare maximization, which is the principal's objective. To avoid such moral hazard, adopting a contract theory approach, we first propose a dynamic incentive and control synthesis problem, where each agent and the principal maximize their own long-term average reward, respectively. We next derive an optimal contract condition and stability condition analytically. Finally, the performance of the proposed contract mechanism is numerically verified through simulations with a four area power network model.

Keywords: Robotics and Motion Control, Mechatronics
Abstract: Our previous study discovered that small particles scattered at the bottom of a water-filled rectangular container align at specific positions when the particles are subject to standing wave induced by laterally oscillating the container at specific frequencies. Also, we identified a method to theoretically estimate the oscillation frequency for aligning the small particles and the positions where several particle lines appear. As the next step, we are currently developing novel micromanipulation methods with this unique phenomenon targeting submillimeter-scale particles. In the present study, this alignment phenomenon is further studied to reveal the potential, especially focusing on the distribution of lateral fluid velocity in vertical direction, the amplitude of lateral oscillation, and the liquid height which have never been discussed in our previous study. This paper examines their relationships with the alignment condition and discusses the applicability of these findings obtained in the present study to develop novel micromanipulation methods of small particles.

Keywords: Nonlinear Control, Robotics and Motion Control
Abstract: This paper studies the trajectory tracking control problem for a vertical rolling disk on an arbitrary smooth surface in Re^3. The disk is assumed to roll without slipping about its axis and turn about the surface normal. A nonlinear controller is designed for the trajectory tracking of the disk based on a kinematic model that is globally defined on the manifold without singularities or ambiguities. The theoretical results are specialized for two different surfaces; a flat surface and a paraboloidal surface. Simulation results are included to illustrate the effectiveness of the tracking control law on the smooth surfaces.

Keywords: Robotics and Motion Control
Abstract: This paper is mainly to solve the path planning problem of swarm robots. If swarm robots work in the same environment at the same time, it is necessary to have appropriate planning methods to enable individuals in the group to complete their tasks. The solution is to use the artificial bee colony algorithm for the problem of robot path planning. Because the artificial bee colony algorithm has a high efficiency to solve the optimization problem, it tries to solve the problem of robot path planning in a distributed manner. The concept of organizing robots achieves efficient task of information processing and group working in the environment.

Keywords: Robotics and Motion Control
Abstract: In this paper, a set of flywheel drive system based on phase-locked loop (PLL) is designed and implemented. The drive motor of the steady speed flywheel is a brushless DC motor (BLDCM). Two drive modes of the motor, sine wave drive and square wave drive are implemented to compare steady speed performance. The resolver is used to obtain higher-precision position. PLL control strategy is used to replace the closed-loop PI control method, which eliminates strong saturation characteristic and large overshoot in the system. In the design of PLL, ten-state PFD is used to eliminate glitch and speed up the response of the system. The loop filter (LF) of the PLL is designed to improve system performance. The digital PLL controller is implemented by FPGA. In addition, the phase noise of the PLL system is analyzed and suppressed. The PLL control system is modeled and implemented. The simulation and experiments show that sine wave drive has higher steady speed accuracy than square wave drive. Experiments verify that in the PLL control flywheel drive system, the error of steady speed is less than 0.03%.

Keywords: Robotics and Motion Control, Autonomous Systems, Control Applications
Abstract: The dynamics of an underactuated mechanical system, e.g. quadcopters, naturally evolve on a nonlinear manifold. Hence, differential geometric modeling concepts are better suited to arrive at the set of equations describing the dynamic behaviour of such systems. Resorting to a differential geometric approach, a generalized dynamical model for a quadcopter is proposed in this paper. The rotation matrices in forward kinematic map are represented using Euler angles. The modeling has been presented on a single chart. The Riemannian metric being a crucial ingredient for model development on a chart is derived from the total kinetic energy. A complete set of dynamic equations of motion neglecting external disturbances is derived for the quadcopter by representing the Levi-Civita affine connection in local coordinates. Further, based on the derived model, a proportional-derivative (PD) controller is designed for attitude stabilization. Simulation studies validate the proposed model and also demonstrate its robustness to wide variations in initial conditions.

Keywords: Biological Systems, System Biology
Abstract: Boolean networks are considered as popular formal models for the dynamics of gene regulatory networks. There are many different types of Boolean networks, depending on their updating scheme (synchronous, asynchronous, deterministic, non-deterministic), such as Classical Random Boolean Networks (CRBNs), Asynchronous Random Boolean Networks (ARBNs), Generalized Asynchronous Random Boolean Networks (GARBNs), Deterministic Asynchronous Random Boolean Networks (DARBNs), Deterministic Generalized Asynchronous Random Boolean Networks (DGARBNs). An important long-term behaviour of Boolean networks, so-called attractor, can provide valuable insights into the origins of cancer. Many studies have been done on attractors in CRBNs and ARBNs. However, in our best knowledge, there is no study on attractor detection in GARBNs, DARBNs, or DGARBNs. In this paper, we study properties of attractors in GARBNs, their relations with attractors in CRBNs, also propose different algorithms for attractor detection. Experiments are conducted on real and artificial networks to compare the performance of the proposed algorithms.

Keywords: Biotechnology
Abstract: Blood glucose prediction is now valuable for diabetes patients to control the blood glucose level and then manage it. However, it may have the problem of insufficient modelling data and modelling burden if a completely new model has to be developed for every new subject. This work proposes a data-driven transfer learning method to quickly get the new prediction model for new subjects based on other subjects’ models and a small number of new data. Besides, an incremental learning method is developed to consecutively improve the new model with more new data available. By simultaneous analysis of transfer learning and incremental learning, the prediction model for new subjects can be readily obtained with few calculation efforts and meanwhile the accuracy is guaranteed. Finally, the proposed method is compared with the general model and individual model with the data from the UVA/PADOVA Type I Diabetes Simulator. The proposed method has shown superiority for both calculation efforts and accuracy.

Keywords: Biotechnology, Control Applications, Identification and Estimation
Abstract: This paper presents a fully automated DNA sample analysis technology, derived for analyzing images acquired from Atomic Force Microscope (AFM) systems, sampling on long-chained biopolymers such as the string-like DNA strands. Since AFM systems are capable of acquiring nanometer resolution images, and these images are embedded with information, such as the biopolymer Worm-Like-Chain (WLC) models geometric characteristic. These include the DNA's contour length (lc) and persistence length (lp). However, since the images acquired by the AFM system are digital by nature, it is necessary to develop a reliable estimation method to accurately retrieve the features of the original biopolymers. In order to accurately retrieve lc and lp, this paper details two crucial problem that was never dealt with before: the DNA self-crossing tracing problem and the DNA contour tangent analysis. On the first issue, bio-polymer self-cross tracing from digital image are dealt with from scanning small grids of the whole image window, and by utilizing the geometric relationship between image pixels, crossing is automatically identified from database matching, along with its trace analyzed. Where its digital Chain-Code (CC) is updated and reflects its head to tail trace. For the second issue regarding contour tangent, a novel directional sorting database is also created for the digital pixel components, where elements of the contour are individually verified for reconstructing its original angle

Keywords: Biotechnology, Control Applications, Observer Design
Abstract: A novel medical device allows the surgical treatment for patients with recurrent Otitis Media with Effusion to be conducted in an office setting under local anesthesia. For a conscious patient, the patient-instrument stabilization is critical for the surgical procedure as any small head movement might cause trauma to the tympanic membrane and the delicate ossicles chain in the middle ear. A two-pronged approach, consisting of a patient-centric method and a device-centric method, is presented in this paper. The patient-centric methods aim to affect the psychological state of the patient positively so that he/she feels calm and settled during the operation; and through a game with feedback mechanism, actively maintains his/her head still to gain reward points. The device-centric approach mitigates any small movements the patient might still make by building a control system that views any head movement as disturbances and compensate the disturbance through disturbance-observer based motion compensation. Through this strategy, the patient-instrument stabilization is achieved.

Keywords: Environmental and Bioengineering, Biotechnology, Control Applications
Abstract: Abstract—Mental health problem has increased as a social problem in many mature countries. It expected that patients himself/herself recognizes and controls of their condition through daily used devices by various countermeasures, and screening techniques are also useful for the patients himself/herself. We have been researching smartphone applications (MIMOSYS) that estimate health condition such as depression state and stress state from voice. Vocal analysis using speech has the feature that it can be easily done with less burden on the implementers. In this research, in order to examine the effectiveness of the estimation of the mental health condition from the voice by MIMOSYS in the iOS device, the analysis result of MIMOSYS was compared, and the effectiveness was examined by using the sound recorded by various iPhone devices. First of all, a healthy person recorded a voice read aloud of fixed phrases. Next, the recorded sound was reproduced from the speaker and recorded as a control using the recorder (R-26), the microphone (ME-52W) and each iOS device. Then, with the recorded voice at the microphone as the control, the recorded voice at each iOS device and the MIMOSYS analysis result compared and examined. As a result, a high correlation found with the result of recording and analysis with the microphone in any iPhone device, and it was confirmed that estimation of the mental health condition by MIMOSYS speech could be performed without problems on the iOS de

Keywords: System Biology, System Theory, Biological Systems
Abstract: Recent progresses in research and development of directed human protein-protein interaction (PPI) networks have opened a great opportunity for the uncovering of possible effects that a drug has during the drug therapeutic processes. Such PPI networks can be used to help researchers predict cell signaling pathways and gain better understanding about drug healing and targeting mechanisms. This paper aims at examining the use of network theory approach to study and analyze the dynamics and treatment of disease in samples of PPI networks. In this paper's analysis, the so-called “disease nodes” are defined as nodes that are responsible for the spreading of the considered disease. By combining the concept of network output controllability with an approach for network simplification, this paper shows that the number of controllable disease nodes for a particular disease can be predicted using a relatively simple computational method. Furthermore, the analysis reported in this paper indicates that a positive linear relationship between the number of controllable disease nodes and the relative efficacy of the used drug is statistically significant in term of drug targeting scheme. While the availability of directed human PPI networks is currently remains incomplete, the results reported in this paper can further corroborate the advantages of using concepts and techniques from control theory and network science in the study and research on drug discovery.

Keywords: Autonomous Systems, Adaptive Control and Tuning, Robust Control
Abstract: In this paper, we propose a model-based diesel engine air path control system using the simple adaptive control scheme. By using this proposed control system, we can design a robust controller with respect to change of environment, disturbances and nonlinearity of the air path system. We confirm the effectiveness of this proposed system by the simulation using an air path model based on physics and the experiment.

Keywords: Autonomous Systems, Cooperative Control
Abstract: An algorithm for autonomous bird deterring using teams of unmanned aerial vehicles (UAVs) is being proposed in this paper. Birds cause significant damage to commercial crops globally. A bird deterring system using autonomous UAVs can potentially overcome the limitations of current bird management methods. The problem of controlling UAVs to deter birds is formulated as a model predictive control problem. Occupancy gird map is used to construct a world model which represents the system's knowledge of the current location of target birds. A bird behaviour model and a sensor model are derived based on experimental results from a series of field trials. The future state of the target is estimated by propagating the centralised world model in time. Cooperative deterring missions are planned autonomously by optimising cost functions assigned to each UAV. A series of simulated scenarios are used to demonstrate the effectiveness of the system at driving UAVs to protect an 8 hectare area from multiple bird flocks. Furthermore, it is demonstrated that by cooperating a team of two UAVs, the same bird deterring mission can be completed more efficiently compared to using a single UAV.

Keywords: Autonomous Systems, Robust Control, Observer Design
Abstract: This paper proposes hovering flight control for a quadrotor UAV with slung load. A dynamic model considering the states of both quadrotor and slung load is established based on Euler-Lagrange equations. This model is simplified to several linear subsystems via reasonable assumptions. These subsystems are proved completely controllable. A robust H_infty controller is designed by utilizing the estimated states of the observer. Due to the lack of sensors to measure the swing angle of the slung load, a reduced order state observer is constructed to estimate these states. The validity of the proposed controller and observer is verified by simulation results.

Keywords: Control Applications, Autonomous Systems, Transportation Systems
Abstract: It is expected that the wide spreading of automated vehicles will make the modern transportation safer, faster and smarter. However, there are still some essential issues to be solved for keeping the driving safety, especially the way to avoid potential collisions with oncoming vehicles at an intersection. This paper proposed a speed control strategy to assist automated buses while crossing intersections, based on the predictions of the motions of oncoming vehicles. The speed of the automated ego vehicle would be optimized to minimize the risk of collision with the oncoming vehicles. Monte Carlo simulations were performed to evaluate the performance of the proposed method. It was demonstrated that the proposed speed control strategy was effective in reducing the risk of collision while passing through intersections.

Keywords: Hybrid Systems, Autonomous Systems, Decentralized Control
Abstract: In this paper, we propose a multi-modal control approach for flocking in the face of obstacles. Our approach improves the resilience of the flock to the problem of fragmentation which exists within many current flocking approaches. In our method, the flocking agents dynamically change mode and adapt their flocking behaviour based on their current context. An initial indication of the efficacy of this method is determined through simulation. Our simulations show that this approach produces flocking behaviour which is more resilient; resulting in fewer collisions, reduced fragmentation, and a more cohesive flock when compared to existing techniques.

Keywords: Computational Intelligence, AI and Expert Systems, Human-Machine Systems
Abstract: The number of elderly persons living alone increase year by year. They are lonely, and they need some supports. Recently, they can get some supports and they can speak to another person using the internet. However, a lot of elderly person cannot use the internet. Therefore, in order to cope with this issue, we propose dialogue system without the internet. The proposed system has three features. First, it can work without internet. Second, it provides a lot of opportunities to speak for elderly person. Third, it can distinguish multiple meaningful words. As a result, the proposed system was able to work without the internet. Furthermore, the system provides a lot of opportunities to speak for users. And, the system can distinguish words with multiple meanings better than conventional systems. And, the system is evaluated by co-occurrence network. Due to that evaluation, I found various application examples. First, proposed system is able to change personality by changing percentage of module activation. So, it is possible to develop a system suitable for each user. Second, proposed system can be applied for early detection and level diagnosis of dementia patients. In conclusion, the system has the effect that a lot of words are used in conversation. In other words, it can provide stimulating conversation better than conventional system. Therefore, it is suitable as a talker for elderly person living alone.

Keywords: Control Applications
Abstract: Atomic force microscopy (AFM) is a powerful instrument that has the ability to characterize sample topography on nanoscale resolution and is widely used in different fields, such as nanotechnology, semiconductor, MEMS, bioscience, etc. However, the main drawback of AFM is the low scanning speed, which restricts the applications in some cases. Conventionally, the scan trajectory is raster pattern under uniform speed which is hard for fast scanning and is inefficient. In this paper, we propose a fast scanning algorithm combining on-line variable speed scan and machine learning based feedforward control. The feedforward signal is based on a Gaussian process model which can be trained from previous image and is able to predict sample topography. Thus, the feedback controller only needs to correct difference between the prediction and real scan data, which can increase scanning speed. The on-line variable speed algorithm would adjust scanning speed based on the instant error and the predictions of the Gaussian process model. Therefore, the scanning process would be sped up at regions with small height change and slow down before where height changes dramatically, such as edges and sidewalls. The experiment results demonstrate that our proposed method can increase image performance and save scanning time remarkably.

Keywords: Control Applications
Abstract: In order to reduce the collision shock and risk of injury to an infant in an in-car crib (or in a child safety bed) during a car crash, it is necessary to limit the force acting on the crib below a certain allowable value. To realize this objective, we propose a semi-active in-car crib system with the joint application of regular and inverted pendulum mechanisms. The crib is supported by arms similar to a pendulum, and the pendulum system itself is supported by arms similar to an inverted pendulum. In addition, the arm acting as a regular pendulum is joined with the arm acting as an inverted pendulum through a linking mechanism for simplicity, and the friction torque of the joint connecting the base and the latter arm is controlled using a brake mechanism, which enables to maintain the deceleration of the crib at around the target value. The present work focuses on the semi-active control with the absolute crib horizontal acceleration feedback derived using the sliding mode control theory for the high performance control. Further, a control experiment using scale model is conducted to confirm the effectiveness, and some results are reported.

Keywords: Control Applications, Robust Control, Linear Systems
Abstract: We study a specific feedback stock trading rule, the simultaneously long short (SLS) strategy. This strategy is known to yield positive expected gains when the underlying stock returns are governed by a geometric Brownian motion or by Merton's jump diffusion model. In this paper, we generalize these results to a set of price models called essentially linearly representable prices that are given by means of a set of stochastic differential equations based on (semi)martingales. Particularly, we show that the SLS trader's expected gain is almost always positive and that it does not depend on the chosen price model but only on the trend. The basic novelties of this work are, first, the extension of the results in the literature to a set of SDEs and, second, that we do not need a solution of the SDEs, but we work on the level of SDEs directly, i.e., also for SDEs without or with unknown closed-form solutions positive SLS trading gains can be proven.

Keywords: Nonlinear Control, Adaptive Control and Tuning, Fuzzy Neural Systems
Abstract: This research aims at establishing a distribution adaptive tracking strategy for a class of high-order non-strict feedback uncertain nonlinear multi-agent systems. In each follower dynamics, the virtual and real control items are positive odd integer power functions instead of common linear items. In addition, each follower's dynamics appears in non-strict feedback form. In controller design process, adding a power integrator approach is adopted to overcome the difficulty caused by high-order structural feature. Radial basis function neural networks are adopted to approximate the unknown nonlinearities. Further, by adaptive neural backstepping technique an adaptive tracking proposal is proposed to ensure that the signals in the whole closed-loop system are bounded and all the outputs of followers synchronously track the leader's signal. A numerical simulation is included to further verify our results.

Keywords: Linear Systems, Complex Systems and Networks
Abstract: This paper addresses the problem of establishing stability of 2D mixed continuous-discrete-time systems. A novel linear matrix inequality (LMI) condition is proposed based on the introduction of a complex linear fractional representation (LFR) of the systems and on the use of complex Lyapunov functions depending rationally on a parameter. Promising results are obtained in terms of computational burden. Indeed, as shown by various examples with small and large dimensions, the computational burden of the proposed LMI condition may be rather smaller than that of other existing LMI conditions.

Keywords: Linear Systems, LMIs, Observer Design
Abstract: In this paper, the minimal order observer based H-infinity controller design is studied. The state space representation of the minimal order observer based controller is complicated and involves the product and quadratic terms of the decision variable matrices, i.e., the state feedback and observer gains. To make the design problem as a bilinear matrix inequality (BMI) problem, we represent the minimal order observer based controller by a descriptor representation, where the decision variable matrices are affinely involved. This descriptor representation enables us to design controllers by applying the overbounding approximation method for general BMI problems. The effectiveness of the proposed design procedure is demonstrated by a numerical example.

Keywords: Linear Systems, Multivariable Control
Abstract: The linear matrix inequalities formulation is proposed to discrete-time linear positive system control design, mounting system matrix structure constraints into design conditions. Coupling together the set of linear matrix inequalities warranting the system positive structure, and the additional inequality guaranteing asymptotic stability of the controlled system, the design conditions are outlined to construct the positive closed-loop scheme with a state-feedback positive control law gain. Using the proposed method, the closed-loop system state, after reaching it, remains in an ellipsoidal positively invariant set. The proposed approach is numerically illustrated.

Keywords: Linear Systems, Switched Systems
Abstract: This paper studies exponential stability and fault tolerant control for linear time-invariant (LTI) positive system with controller failure. Through dwell time (DT) and mode-dependent average dwell time (MDADT) approach, sufficient conditions of globally uniformly exponential stability (GUES) are proposed. We treat the mode changing between controller work and failure as a switch. The main idea is using switching behavior to compensate the increment of Lyapunov function (LF) produced by controller failure, introducing the multiple linear copositive Lyapunov function (MLCLF) method to obtain less conservative results. By solving the linear programming problem, the switched positive system can be GUES under the constraint of DT and MDADT. On this basis, a fault tolerant controller is designed. Numerical examples are provided to show the effectiveness of the results.

Keywords: Linear Systems, System Theory
Abstract: The model matching is a useful tool for control applications. The desired dynamics of practical systems often depends on time-varying parameters. This paper gives the feed forward compensator so that the model matching for the reference model with time-varying parameter is attained. In deriving the compensator, we propose the sufficient condition so that the feed forward compensator exists. The results are the natural generalization of the SISO problem and the LTI case. Numerical simulations also confirm the effectiveness of the obtained results.

Keywords: Filtering, LMIs, Nonlinear Control
Abstract: This paper concerns with the dissipative filtering problem for nonlinear singular system with stochastic perturbation. By considering the uncertainties exist in membership functions, the nonlinear system is modeled by interval type-2 fuzzy (IT2F) stochastic singular system. Based on the Lyapunov stability theory, a sufficient condition is utilized to prove that IT2F filtering error system is mean-square admissible and strictly dissipative. And the corresponding solution of the dissipative filter model is obtained by solving a set of linear matrix inequalities. A numerical example is provided to prove the correctness of the obtained result and the designed method.

Keywords: Filtering, Networked Control Systems, LMIs
Abstract: This paper investigates the problem of robust H-infinity filtering for delta operator systems with random delays and communication constraints. The considered networked control systems are modeled as parameter uncertain systems with one-step random delay, which is formulated by a Bernoulli stochastic process. Sufficient conditions of stochastic stability for the filtering error system with an H-infinity performance are presented by Lyapunov-Krasovskii functional in delta domain and linear matrix inequalities (LMIs). The parameterization expression of the designed H-infinity filter is also obtained. The effectiveness of the proposed method is demonstrated by a numerical example.

Keywords: Filtering, Sensors and Senor Fusion, Identification and Estimation
Abstract: This paper is concerned with the distributed estimation problem by using diffusion Kalman filters. Instead of the traditional minimum mean square error (MMSE) criterion, the maximum correntropy criterion (MCC) is used to define a new cost function in the diffusion update step and a fixed-point iteration algorithm is adopted to fuse the intermediate estimates of neighboring nodes. It is shown that the proposed filter is a modified version of the traditional diffusion Kalman filter with adaptive weights and the weights are tuned by using fixed-point iteration. An iterated diffusion Kalman filter is further developed to improve estimation performance. Simulation results show that the proposed approach outperforms existing techniques and the proposed filters can efficiently handle non-Gaussian noise and large variations of measurement noise covariances.

Keywords: Filtering, Signal Processing
Abstract: To deal with problem of complexity in state distribution, a recursive state estimator with high-order moment component form of a nonlinear Markov jump system (MJS) with state variable non-Gaussian. The nonlinear MJS is transformed into a nonlinear deterministic system with high-order moment component form by means of cumulant generating function. Based on the transformed single-mode nonlinear deterministic system, a recursive state estimation in Bayesian framework is given to estimate the error of the state with high-order moment component form. To describe the non-Gaussian state distribution, the generalized Gaussian distribution (GGD) is used which introduces the high-order moment information compared with the first- and second-order information, mean and variance in Gaussian distribution (GD). With the help of high-order moment information such as the skewness and kurtosis in the GGD, the performance of state estimation will be much more precise. The numerical example in the simulation part shows the performance of the state estimation and the comparison of errors in different moment, which demonstrate the effectiveness.

Keywords: Observer Design, Intelligent and Learning Control, Filtering
Abstract: This elaboration presents the method of obtaining experimental data for the neural network training process. A family of data and the initial conditions were obtained by the stabilization experiment of the levitating sphere and by a short term control. On the basis of these data, a structure of the feedback network was developed. The nonlinear autoregresive neural network realizes the task of the velocity estimation of the levitating sphere. The structure of the neural network was assessed and then applied in real time to the stabilization task. The results of experiments were compared with two different methods of a velocity estimation.

Keywords: Signal Processing, Adaptive Control and Tuning, Identification and Estimation
Abstract: In this paper, normalized least mean fourth (NLMF) algorithm which compensates the bias caused by noisy inputs and is robust to impulsive noises is proposed. A bias compensation vector is added to the NLMF algorithm to eliminate the bias and the modified Huber function is used to improve robustness to the impulsive noises. In the elimination process of the bias, the estimation method for the input noise variance is calculated by various methods and compared with each other. Simulations in a system identification context show that the proposed algorithm effectively removes the bias of the noisy inputs and performs correctly in the impulsive noise environment.

Keywords: Identification and Estimation
Abstract: The accuracy of process model has a significant impact on control and optimization. However, for complex time-variant systems, the traditional steady state modeling method is generally not effective on dynamic process. In this paper, a data-driven dynamic modeling method based on nonlinear slow feature analysis (SFA) is proposed to reduce the effect of deterioration with age, such as catalyst deactivation. Variables obtained by known mechanisms are used to consider nonlinear relationships between process variables, then, the SFA is executed to extract slowly changing characteristics, and finally, slow features predict model is constructed. The method is applied to the industrial acetylene hydrogenation reaction process, and it can be confirmed that the proposed method enables models to predict response accurately.

Keywords: Optimal Control and Optimization
Abstract: Mixed integer nonlinear programming (MINLP) problems widely exist in various aspects of an oil refinery, from crude oil supply chain, crude oil scheduling, production process decision-making, to optimal design of heat exchanger network or hydrogen network. Due to the NP-hard nature, many MINLP problems are difficult to solve efficiently and reliably. In this paper, a cooperative algorithm based on an improved particle swarm optimization algorithm (PSO) and linear programming algorithm (LP) is proposed to solve the complex MINLP problems. The PSO-LP cooperative algorithm includes an outer module running the improved PSO algorithm, and an inner module running LP algorithm, for example, the simplex algorithm. The outer module transforms the MINLP problem into a LP problem and passes it on to the inner module. The inner module feeds back the LP results to the outer module to update the MINLP problem. The above procedure iterates continuously to get the optimal solution of the MINLP problem. The proposed PSO-LP cooperative algorithm is verified using various MINLP problems. Results show that the proposed algorithm is computationally more accurate and more reliable than the existing ones.

Keywords: Process and Chemical Systems, Optimal Control and Optimization
Abstract: HYSYS is an important simulation tool for refining processes, in which the basic proportion of key quality components are provided. However, the proportion is often kept unchanged. This often deviates from the actual production, in which the crude oil has become heavier and more inferior. As a matter of fact, the component proportion directly affects the properties of the feed oil and the modeling accuracy. In this paper, to accurately model the operation of a real industrial residue hydrotreating unit in a refinery, a correct method is proposed to calibrate the parameters of residual library basic component based on improved squirrel search algorithm (ISSA). The method takes the ratios of the basic components as the decision variables, and uses ISSA to minimize the deviation of the simulated values of density, sulfur and nitrogen content and the TBP distillation from the lab analysis values. In order to enhance the exploitation ability of the algorithm, an adaptive strategy is used to adjust the scale factor. Moreover, other squirrel position information is introduced for iterative population updating in this algorithm, which can handle the weak exploration ability at the stagnation in late period. Simulation results show that the corrected residual library coefficient can accurately reflect the actual feeding situation of the residue hydrotreating unit, which can ensure to realize high-precision simulation.

Keywords: Sensors and Senor Fusion, Pattern Recognition, Process and Chemical Systems
Abstract: Soft sensors have been widely used to predict the quality of products in industrial hydrocracking processes. However, they are usually characterized with complex nonlinearities and multimodes. Hence, a single global model is often difficult to fully meet the requirement of prediction accuracy. In this paper, a novel fuzzy C-means cluster (FCM) based locally weighted principal component regression (LWPCR) is proposed for soft sensor modeling of an industrial hydrocracking process. First, FCM is utilized to cluster process data into different local clusters with similarity data patterns. When a query sample comes, LWPCR models are then built for accurate modeling in different modes with the most similar relevant samples. At last, the outputs of these sub models are fused to obtain a final estimation value of the query sample, in which the weights are automatically determined by the distances between the query sample and the cluster centers. The effectiveness of the presented approach is verified by predicting the 50% recovery temperature in an industrial hydrocracking process.

Keywords: Automata, Discrete Event Systems, Identification and Estimation
Abstract: In this paper, we investigate the initial-state detection problem in the context of partially-observed discreteevent systems. Specifically, we study the verification of two properties called weak I-detectability and strong I-detectability. Weak detectability captures whether or not the initial-state of the system can be detected via some path, while strong detectability captures whether or not the initial-state of the system can always be detected within a finite delay. In this paper, we provide new verification algorithms for checking these two properties. The idea is to use the reversed dynamic of the system to efficiently estimate the initial-state information. We show that our new results improve the complexity of existing verification algorithms for both properties. We illustrate our results by simple examples.

Keywords: Optimal Control and Optimization, Process and Chemical Systems
Abstract: Optimization of operational parameters is essential for the industrial process, but it is expensive to optimize them with their rigorous mechanism models. Therefore, a series of novel surrogate-based optimization algorithms are popular recently. The main idea of surrogate-based optimization is that the complex mechanism model is replaced by a simple imitator when searching for the optimal solution within a certain region. Usually, this region is created by Latin hypercube sampling and considered as a continuous decision space. However, in practical engineering optimization, the whole operating space is not always continuous, resulting from various type of equilibrium constraints, e.g., material balance and energy balance. To bridge the gap between the requirement of continuity for surrogate- based optimization and the reality of discontinuity on feasible operating space, a multi-block Kriging surrogates-based intelligent optimization algorithm was proposed in this paper. With some prior knowledge on economical operation, the whole discontinuous operating space is divided into several promising continuous subregions. In the simulation study, a set of Kriging metamodels were utilized to represent the rigorous hydrocracker model at different subregions considering the catalyst life. Simulation results indicate the effectiveness of our proposed method, in which the inlet temperatures of all reaction beds were optimized under a given production condition.

Keywords: Robotics and Motion Control, Control Applications
Abstract: This paper integrates a multi-agent broadcast controller with an event triggered distributed controller to solve multi agent motion coordination consensus issue. The broadcast controller has been designed by using the Simultaneous Perturbation Stochastic Algorithm. A distributed consensus protocol is adopted with an event triggering function to achieve local consensus among agents with a less number of communication channel usage. Each agent will receive a feedback signal from broadcast controller to update its position from the target while the communication between agent and its neighbours will happen if satisfying the event triggered condition at event instant time. It has been proven that the system can achieve consensus by utilizing the proposed control method. This result shows that at a certain time and iteration the agent can reach the target while reducing the communication channel usage as well as utilization of energy consumption during the process.

Keywords: Robotics and Motion Control, Control Applications, Real-Time Systems
Abstract: An accurate estimation of angular acceleration signals for robot manipulators is necessary for the improved control performance. In this paper, a complementary estimation method for angular acceleration of robot manipulators under slow sampling rates is proposed. Based on the data-driven system model, a state observer is constructed to estimate acceleration signals, which require fast sampling rates to improve the performance. Since a linear model-based state observer with slow sampling time undergoes low-frequency sensitivity, a complementary scheme combining the finite difference approach and the state observer approach together is proposed. By fusing them, we could obtain an improved estimation of angular acceleration signals with low-frequency robustness.

Keywords: Robotics and Motion Control, Human-Machine Systems, Control Applications
Abstract: In this paper, a novel, inexpensive and reliable stair climbing wheelchair mechanism is proposed. Large variation in inclination angle of a traditional wheelchair while moving on steps posses high risk to the rider's safety. A new active Slider-crank mechanism is used to minimize any variation in inclination angle of the wheelchair during climbing up or down on stairs. This, in-turn, reduces the risk of wheelchair toppling on steps. Kinematic model of the proposed wheelchair is developed and MATLAB simulations are performed to inspect the behavior of wheelchair on stairs. In addition, the mechanism is designed in such a way that the rider can sit towards the motion direction, which adds additional safety to the rider. It is envisaged that the novel step climbing wheelchair will enhance the mobility of the elderly people significantly.

Keywords: Robotics and Motion Control, Nonlinear Control, Discrete Event Systems
Abstract: This paper considers a data-driven formation control problem for a class of unknown heterogenous discrete-time multi-input multi-output (MIMO) nonlinear multi-agent system with switching topology. To realise the formation, a distributed data-driven control algorithm is proposed by using the model-free adaptive control (MFAC) method. Only the inputs/outputs data of the neighbouring agents and the desired signal are used in the proposed control algorithm without utlizing the mathematical model of the multi-agents. The formation is accurately realised with rigorous demonstration by virtue of the proposed control algorithm, based on the algebraic theory and matrix theory. A numerical simulation is presented to illustrate the obtained results.

Keywords: Robotics and Motion Control, Nonlinear Control, Optimal Control and Optimization
Abstract: This paper presents a nonlinear model predictive control problem of a fully-actuated hexrotor unmanned aerial vehicle. First, mathematical preliminaries of rigid body dynamics are described, and the hexrotor dynamics on the special Euclidean group SE(3) is formulated. By using log operator on the special orthogonal group SO(3), the state error is calculated without relying on local representations such as Euler angles. A quadratic cost function is then defined using the presented state error function, and a nonlinear optimal control problem is considered. Nonlinear model predictive control scheme enables to consider input constraints and the cost of gravity compensation. The numerical simulation result shows that the model predictive controller chooses energy-efficient input for each structure considering its acceleration characteristics.

Keywords: Control Applications, Optimal Control and Optimization, Robust Control
Abstract: There are some problems in model-based active vibration controls. It is required to maintain performance and stability of a closed-loop system when characteristics of a target structure change. In addition, the control performance and stability depend on the accuracy of the model. In this study, model-free vibration control system to enable vibration suppression of arbitrary structures is proposed. Vibration suppression can be realized by using the model of virtual structure corresponding to the controlled object. The effectiveness and robustness of the control system are validated by control simulations with several types of controlled objects using a unique mode-free controller.

Keywords: Identification and Estimation, LMIs, Robust Control
Abstract: This study investigates the position-tracking and attitude-tracking control problem for the Lead-Wing close formations in the presence of actuator faults. An output feedback fault-tolerant control design is developed to achieve the tracking property and to guarantee the robust stability of the close formation system while maneuvering the Lead unmanned aerial vehicle. The sufficient condition of the feasible solution of the proposed nonlinear inequalities is given. Simulation results of the close formations validate the effectiveness of the output feedback fault-tolerant control strategy.

Keywords: Linear Systems, Robust Control
Abstract: A mixed H2/H-infinity balanced realization is proposed for discrete time systems, where an H2 Lyapunov and an H-infinity Riccati equations have the same positive definite diagonal matrix. Based on this realization, a balanced truncation is possible, which gives a reduced-order model. It is shown that the H2 norm of any reduced-order model based on this realization is not greater than that of the original model. The H-infinity norm of the reduced-order model is also bounded by the value which appears in the Riccati equation and gives an upper bound of the H-infinity norm of the original model. A key idea for proving these properties is to employ the bounded real lemma instead of the Riccati equation. Upper bounds of the H2 and the H-infinity norms of the error system between the original model and the reduced-order model are also established. These properties are illustrated through a numerical example.

Keywords: Robust Control, Fuzzy Neural Systems, Process and Chemical Systems
Abstract: This paper deals with the observer based robust control design problem for multivariable laboratory process with time-varying uncertain parameters. By using the Young relation, and the cone-complementary, the stabilization problem is then converted into convex optimization one with LMI constraints. PDC controller and observer gains are obtained under ℒ2–bounded disturbance by set feasible solutions of LMI conditions. The effectiveness of the proposed design methodology is illustrated by considering a Quadruple-tank systems.

Keywords: Robust Control, Linear Systems, Control Applications
Abstract: This paper presents necessary and sufficient conditions for deriving a strictly proper dynamic controller which satisfies the negative imaginary output feedback control problem. Our synthesis method divides the output feedback control problem into a state feedback problem and a dual output injection problem. Thus, a controller is formed using the solutions to a pair of dual algebraic Riccati equations. Finally, an illustrative example is offered to show how this method may be applied.

Keywords: Automotive Systems, Identification and Estimation, Markov Processes
Abstract: Driving cycles are commonly used to estimate emissions and driving behavior. This is especially relevant for electric vehicles (EVs) as their power and range is limited. Typically, range optimization and control methods are based on approximating the real driving profile of the user. However, the driving profile might vary depending on the vehicle type, street conditions, and driver. Therefore, driving cycles build the basis for EV related control topics. However, most cycles are developed for emission testing of four-wheelers not for EVs or electric two-wheelers (E2Ws). Further, standardized driving cycles are often developed for European or American street conditions not Asian ones. Therefore, a first driving cycle for E2W in Shanghai is described using different driving cycle construction methods. More than 100 E2W driving cycles are collected for a daily commuting scenario in Shanghai. E2W in Shanghai are clustered into different power levels to compare the results. Further, four driving cycle construction methods are used to construct driving cycles and compared for their applicability. As a result, the constructed driving cycle using Markov chains is used as Shanghai E2W Driving Cycle (SE2WC). The SE2WC shows similarities to other Asian city cycles but also lower speeds and accelerations due to the lower E2W power.

Keywords: Automotive Systems, Identification and Estimation, Observer Design
Abstract: In this paper, we propose a vertical forces estimator based on Unscented Kalman Filter (UKF). The vehicle dynamics is so complicated that it is common to estimate its motion using its reduced model based on a decoupled model consisting of a lateral dynamic model and a longitudinal dynamic model on an even road. The decoupled model is not effective in estimating vehicle motion when the vehicle is severely maneuvered causing high rolling motion. In this paper, we proposes a new estimator to estimate role angle and lateral acceleration ln using UKF algorithm for 3-Degree-Of-Freedom Model. Then a lateral load transfer model and a longitudinal model are used to estimate the vertical load force on each tire. From numerical simulation study using MATLAB/CarSim, we observed that there were good agreements between ground truth and the estimated vertical tire forces even the vehicle was driven at 60 km/hour speed by a sine wave steering wheel angle with +/- 60 degrees at 0.1Hz on various driving roads.

Keywords: Automotive Systems, Intelligent and Learning Control, Nonlinear Control
Abstract: The paper presents the design of a nonlinear dynamic neural network-based feedback linearisation (DNNFBL) control scheme for an antilock braking system (ABS) incorporated with a half-car active vehicle suspension system (AVSS). Highly nonlinear mathematical model for the integrated ABS/AVSS was derived from first principles. The dynamic neural networks were trained offline using the quasi-Newton algorithm. Simulation results reveal that the DNNFBL controller exhibits faster response times, greater power being applied to the actuators, and reduced braking time and distance in comparison to the PID and PDF-controlled systems.

Keywords: Automotive Systems, Robust Control, Linear Systems
Abstract: In this paper, control of semi-active suspension with Magneto-rheological (MR) damper is studied to improve the ride comfort of vehicles. The controller is divided into two layers: a constrained Hinf output feedback control and an MR damper control. Constrained Hinf output feedback control provides the reference of the MR damper control. Ride comfort is chosen as the output performance of Hinf, and suppressing the hop of the wheels, suspension stroke limitation and saturation limitation of MR damper are time-domain constraints. A feedforward and feedback control law is applied to control the MR damper precisely. Compared with passive suspension system, the proposed scheme can achieve better ride comfort as well as constraint satisfaction.

Keywords: Adaptive Control and Tuning, Automotive Systems
Abstract: With the development of technology, the paradigm of the vehicle powertrain is changing. The engine is becoming smaller and smaller due to environmental restrictions. However, a small size of the engine has limitations on the variety of engine sound. Thus making artificial engine sound with an extra audio system will make the car more sporty and active. Nowadays, many of the vehicle companies are adopting active sound design(ASD) into their vehicle. In this paper, a new adaptive algorithm for the ASD is proposed. This algorithm will enable the engine sound to reach the specific noise level that the sound designer is planned to. Through the numerical simulation and the experiment results will be shown through this paper.

Keywords: Control Applications
Abstract: The accuracy of the position and attitude of the acoustic array is the precondition of underwater acoustic source identification and detection based on array signal processing. In order to provide accurate reference coordinates for the array, the Strapdown Inertial Navigation System (SINS) / Sonobuoy / magnetic compass integrated positioning and orientation system is proposed to use for it. SINS is the leading system, and sonobuoy and magnetic compass are the assistant systems. The SINS error equation is used as the state equation of the integrated system. In order to improve the observability of the attitude and position simultaneously, the measurement equation containins both the attitude and position information, and the observability of the system is analyzed. Taking the acoustic vector hydrophone array hoisted under the hull as the research object, the simulation results show that the position and attitude of the array can be determined accurately.

Keywords: Control Applications
Abstract: As a promising untethered actuator, an ultrasound has been researched for several applications such as drug delivery, cell manipulation, and microrobot actuation. When micro objects are located in the standing wave field, the particles are trapped at the low acoustic potential position by the generated acoustic radiation force. Based on this principle, the ultrasound can move, trap, sort, and align a single micro device or particles without physical wiring onto the target object. In this study, we present a micro particle position control method by using ultrasound wave frequency control through an ultrasonic transducer, especially for the gravity directional movement. The acoustic potential of the medium was changed with respect to the standing wave position by controlling the excitation voltage frequency to the ultrasound transducer. In experiments, a single transducer with a resonance frequency of 950 kHz was used in the medium (water) to manipulate a micro particle. It was carried out to characterize the controllability of manipulation, and capture the micro particle in the medium. The results showed that the proposed system can successfully move up and down the micro particle by using the suggested ultrasound frequency control method.

Keywords: Control Applications, Delay systems, Energy Technology
Abstract: In this research, a model for ocean thermal energy conversion (OTEC) plant using double-stage Rankine cycle to take time delays into account is proposed. The model is constructed by combining the conventional model with the characteristics of separator and working fluid tank. In order to confirm the effectiveness of the proposed model with time delays, a numerical simulation was carried out. The simulation result indicates that the time delays were successfully reflected on most of the calculated results.

Keywords: Control Applications, Process and Chemical Systems, Sensors and Senor Fusion
Abstract: Production process in a fertilizer plant requires some physical variables, which are called primary variables, to be measured indirectly due to the limitation of measuring conditions and methods. Alternatively, virtual sensing system is used to predict and estimate these primary variables by using information from the secondary variables which are relatively easier to be measured. A development of virtual sensing system or virtual sensor to estimate benfield solution concentration of a stripper unit in a fertilizer plant is introduced in this paper. The virtual sensor consisting of a Diagonal Recurrent Neural Network (DRNN) method is used to model the relationship of primary and secondary variables, coupled with the Extended Kalman Filter (EKF) that will estimates the primary variables based on the measurement made by the secondary variables. During the learning process of DRNN, Particle Swarm Optimization (PSO) is proposed, to determine the optimal weights of the network. Using the estimated primary variable, a PID control strategy is designed and optimized by PSO to obtain an optimized controlled response. Using the real-time operational data acquired from the stripper unit of a fertilizer plant, results show that the proposed control strategy is able to control the benfield solution of the stripper unit satisfactorily.

Keywords: Multivariable Control, Control Applications, Optimal Control and Optimization
Abstract: In this paper, an optimal control system of the whole process of polyester fiber is proposed. Based on the hierarchical Bayesian CTAP model, the system divides fiber production planning into on-demand manufacturing and product development. On-demand manufacturing includes three steps. Firstly, the system collects data from raw material database, equipment database and process database in database cluster. Secondly, immune endocrine neural network (IENN) is used to get the system optimal product process parameters. Finally, a real-time database is used to compensate for the forward error of the production process. In the process of product development, the database of raw materials, equipment and process are regenerated based on the parameter sets of polymerization sub-model, melt transport sub-model and spinning sub-model. Then, IENN is used to get the optimal product process parameters, and a real-time database is used to compensate for the reverse error of the production process. This system can quickly respond to fiber manufacturing needs. On the premise of diversification of enterprise demand and differentiation of competition, it can save energy and reduce consumption, ensure production capacity in a short time, and produce different kinds of polyester fibers with lower cost and higher quality.

Keywords: Control Applications, Complex Systems and Networks
Abstract: This paper is concerned with tool attitude control of directional drilling systems, which plays a key role in guaranteeing desired drilling trajectory. The coupling of inclination and azimuth motions are taken into account in the motion model, where the azimuth one involves some time-varying parameters. To ensure satisfactory control precision, an output-feedback compensation control scheme is developed based on a motion model established from Taylor expansion. Stability analysis is sequentially made for the resulting inclination and azimuth closed-loop system. At last, some simulations show that the compensation controller can achieve higher control accuracy than a proportional integral controller.

Keywords: Optimal Control and Optimization, Mechatronics
Abstract: In this work, the motion control of a double-cone on tilting divergent-convergent straight rails is investigated. Concretely, rails are fixed on the hull of a ship, which is excited to roll along its longitudinal axis, by using a pendulum. Movies of the double-cone moving on the rails were shot, and the total travelling time was determined through the slow motion processing of the films, for various values of the maximal inclination angle of the hull, start positions of the double-cone on the rails, and energies introduced into the system by the pendulum. In order to maximize the kinetic energy of translation of the double-cone, the optimal geometry of the rails was settled. Dynamical properties of the double-cone mechanism, i.e., the apparent spring constants and damping ratios were identified, under the free movement and forced excitation. Results obtained are discussed from an applicative standpoint.

Keywords: Optimal Control and Optimization, Networked Control Systems, Linear Systems
Abstract: This paper addresses the issue of discrete time-invariant optimal output feedback control across an unreliable communication channel in the context of networked control system where random packet loss of control signal occurs. By proposing a unified performance criterion, the work aims to integrate the designs of controller and compensator by connecting the latter and the performance index under the output feedback LQR setting. A new set of design equations for the controller and the compensator satisfying optimality conditions is derived which then is followed by a convergent algorithm that solves the design equations for the gains. Finally, a numerical example is given to compare performances and validate the proposed approach.

Keywords: Optimal Control and Optimization, Nonlinear Control
Abstract: This paper intends to generate an optimal energy-based controller for swinging up a rotary inverted pendulum, also known as the Furuta pendulum, by applying the Bayesian optimization called Entropy Search. Simulations and experiments show that the optimal controller has an improved performance compared to a nominal controller for various initial conditions.

Keywords: Optimal Control and Optimization, Nonlinear Control, Computational Intelligence
Abstract: This paper proposes solutions to finite-time optimal control problems based on numerical Gaussian processes. In the proposed method, Hamilton-Jacobi-Bellman (HJB) equations are formulated using Euler method to be solved by numerical Gaussian processes. In order to incorporate information of systems into Gaussian processes, the covariance functions are structured explicitly by HJB equations.

Keywords: Optimal Control and Optimization, Power Systems and Power Electronics
Abstract: We present a profit maximization issue in a day-ahead electricity market for a participant (aggregator) whose financial condition is considered under the uncertain photovoltaics (PV) power situation. An energy demand/supply profile can be achieved by adjusting the dispatchable devices, e.g., energy storage systems and fossil fuel generators, by applying a prosumption model. We analyze an uncertainty issue brought by the PV profiles, discussing the relationship between the PV profile and the associated profit. Then we propose a strategy, in which the financial condition is considered by constraining the potential profits within one particular acceptable extent. Based on the proposed strategy, the aggregator is supposed to operate his dispatchable devices to pursue the maximum profit by submitting a risk-aware prosumption profile to the market. The simulation results show that under different financial conditions, the aggregator's performances are different and reasonable.

Keywords: Optimal Control and Optimization, Predictive Control, Markov Processes
Abstract: The purpose of this paper is to minimize the total electricity cost while considering comfort in the home. We propose home energy management (HEMS) method by model predictive control (MPC) based on EV state prediction. If charging and discharging can be planned based on EV usage prediction, it can minimize the charging with an external charging station because it can be charged in advance at home. We introduce a Quasi-Markov model in order to predict the use of EV. Finally, we confirm the validity of this research through numerical simulations.

Keywords: Adaptive Control and Tuning
Abstract: In this paper, optimal feedback gain analysis for a class of Euler-Bernoulli beam equation systems with special boundary feedback control is conducted. Using the extremum seeking approach in our investigation to search the optimal feedback gain which can make the system energy quickly reach the exponential stability. Subsequently, we extend the extremum seeking approach to the non-collocated boundary feedback system, which proves that the approach is also feasible for multi-parameter cases.

Keywords: Adaptive Control and Tuning
Abstract: Thermostat has been widely applied to drugs, textiles, food processing and other fields. However, because there is a relatively large time lag of the temperature measurement and coupled with a time-varying large time constant for temperature control in thermostat, the temperature precise regulation in thermostat is a challenge problem. Regular control methods such as PID cannot perform very well. In view of the time delay and large time constant features of thermostat regulation, the stability time of the step response is too long to carry out the step response identification. To identify the process model rapidly and satisfy the limits of safe operation, the relay feedback identification method is applied in this work. By applying the adaptive control strategy, large inertia and time-variant can be overcome. In this work, a model identification method for temperature control of the thermostat with relay feedback identification is proposed. Based on the identified model, an adaptive controller is designed and the initial parameter of adaptive controller is optimized via solving LMI with robust control method. The proposed method has been applied to develop a temperature regulator based on C2B8SNFA MCU (Micro Controller Unit) for thermostat. The real application results have verified the feasibility and effectiveness of the proposed method and the developed temperature regulator for the thermostat.

Keywords: Adaptive Control and Tuning, Distributed Parameter Systems
Abstract: In this paper, we are concerned with the error feedback regulator problem for one-dimensional reaction-diffusion equation with general harmonic disturbance anti-collocated with control, while the tracking error collocated with the control. We construct a target system in order to make the control and anti-collocated disturbance become collocated, and make the measured error become the output. Then the error feedback adaptive servomechanism for the system is given in which the parameters of the disturbance and the reference signals are estimated. The backstepping approach is adopted in the investigation. It is shown that the proposed adaptive control law regulate the tracking error to zero and keep the state bounded.

Keywords: Adaptive Control and Tuning, Energy Technology, Nonlinear Control
Abstract: Previous studies have demonstrated that the electrolyte temperature of an all-vanadium redox flow battery (VRB) has a significant influence on the safety and efficiency of the battery. Therefore, an effective cooling strategy is required, especially for large-scale batteries. In this paper, a dynamic thermal model of a VRB with heat exchangers is presented, in which the internal losses, pump energy losses and reversible entropic heat are taken into account. Based on this model, a robust adaptive backstepping control scheme is proposed so that the stack temperature can track the desired temperature in spite of the presence of unknown parameters, external disturbance and control saturation. To avoid the problem of the ``explosion of complexity'' in the conventional backstepping method, a dynamic surface control technique is introduced in this paper. The anti-windup technique is used to compensate for the effect of control saturation. Simulation results verify the effectiveness of the proposed controller under varying operating conditions.

Keywords: Adaptive Control and Tuning, Identification and Estimation, Linear Systems
Abstract: Feedback error learning (FEL) is a useful tool for designing two-degree-of-freedom (2DOF) control systems. FEL consists of a given feedback block for stability and a feedforward block tuned online for tracking. Recently the author's group has proposed a new FEL tuning law which requires a certain strictly positive real (SPR) condition in the feedback loop. In this paper we discuss relaxation of this requirement by means of filter selection in the FF controller.

Keywords: Adaptive Control and Tuning, Identification and Estimation, Nonlinear Control
Abstract: The algorithms of a reference model adaptive control, an astatic control, and an adaptive control with disturbances estimation are considered. A mobile object is described by equations of kinematics and dynamics in three-dimensional space. The novel structure of the control system is proposed. Asymptotic stability of the closed-loop adaptive system is proved by the method of Lyapunov. Analysis of the errors of disturbances estimator is performed. Simulation and comparative study of the reference model adaptive control, the astatic control, and the adaptive control with disturbances estimation are carried out.

Keywords: Predictive Control, Multivariable Control, Delay systems
Abstract: The actual refining processes exist delays and the control effect is sensitive to the disturbance and the model mismatch, which are likely the reasons of control failure. Motivated by this problem, a feedforward decoupling dynamic matrix control with Smith predictive compensation principle (SF-DMC) is proposed for the heavy oil separated process. In the aspect of algorithm, the dynamic matrix control (DMC) is combined with the Smith predictive principle to compensate the delays and the influence of disturbance. Based on the model analysis, the primary interaction relationships between the manipulated variables and the controlled variables are reserved, and the minor interaction relationships of the manipulated variables and the controlled variables are introduced as feedforward to improve the control performance. Finally, the double-input and double-output classical heavy oil fractionation tower model is used to confirm the effectiveness of the proposed method.

Keywords: AI and Expert Systems, Intelligent and Learning Control, Pattern Recognition
Abstract: Tracking crude oil moisture content in real time is critical in the oil refinery production process. Traditional methods are time-consuming, low precision and lack of mechanism analysis. Aiming to solve these problems, we present an object detection method of crude oil moisture content based on NMR principle and deep learning, which named convex region search. It realizes the location of water information in the crude oil 1H NMR spectrum and classification of all the sample points. Then corresponding regression models are established based on the captured features of disparate categories. The experimental results show that the proposed method can not only locate the position of water peak in NMR spectrum quickly and precisely, but also predict the moisture content value accurately.

Keywords: Identification and Estimation
Abstract: Free lime (f-Cao) content is the key quality indicator in the control and optimization of cement calcination process. However, the cement process has many complex characteristics including large noise, time delay, nonlinearity, and time-varying system. To cope with the above characteristics, this study proposes just-in-time (JIT) learning with empirical mode decomposition (EMD) and database monitoring index (DMI) for free lime prediction. At first, EMD and Savitzky-Golay (S-G) filter can effectively deal with process large noise. Meanwhile, weighted time series analysis and mutual information are used to estimate large time delay. Then, least squares support vector machine (LSSVM) based on JIT and DMI is employed to solve the problems of process nonlinearity and time variation. The proposed strategy is validated on practical data from a cement plant.

Keywords: Adaptive Control and Tuning
Abstract: Model-Free Adaptive Control (MFAC) is a new data-driven control method, which depends only on the input/output(I/O) measurement data rather than the mathematical model information of the actual controlled system. The SISO MFAC based on the compact-form dynamic linearization (SISO-CFMFAC) is a promising approach to control the SISO nonlinear systems. However, the parameters in the SISO-CFMFAC should be tuned carefully before being put into use. Unfortunately, so far the parameter tuning of SISO-CFMFAC is still a laborious, time-consuming and cost-consuming work. In this paper, a novel parameter self-tuning approach of SISO-CFMFAC based on back propagation Neural Network with System Error set as input (SISO-CFMFAC-NNSE) is proposed, and then verified by using a typical time-varying nonlinear SISO system. Results show that the proposed controller named SISO-CFMFAC-NNSE can achieve better control stability and accuracy than the existing controller of SISO-CFMFAC.

Keywords: Computational Intelligence, Process and Chemical Systems, Sensors and Senor Fusion
Abstract: The availability of an accurate model can facilitate tasks such as design or monitoring in chemical industries. A well-constructed model is invaluable but it can be costly to construct due to the expense in acquiring the labeled data. In the effort to reduce cost of building model, transfer of information using available sources has been considered. However, only the linear transfer has been considered for chemical processes in the reported literature. Moreover, the unlabeled data problem for transfer is not considered too. In this work, a new Gaussian process (GP) model based transfer learning approach for modeling is proposed. The aim is to leverage the statistical approach of the GP to transfer the knowledge from some available data of other processes for both labeled and unlabeled data. These data is used to provide information that is lacking to the process of interest. Case studies are presented to demonstrate the features of the proposed method.

Keywords: Decentralized Control, Process and Chemical Systems, Multivariable Control
Abstract: Processes involved in chemical industries are generally of Multi-Input Multi-Output (MIMO) Systems configuration. Most of them are non-linear in nature. Centralized and decentralized control systems are used for obtaining desirable output from them. MIMO Systems having strong interactions need to go for a centralized control mechanism, whereas the mild interacting systems can be controlled by decentralized controllers. This paper presents Linear Algebra-based Controllers (LACr) for two chemical processes, Exothermic reaction and Ideal CSTR. The controller designs are based on First Order Plus Time Delay (FOPTD) models obtained by the approximation of real processes, which are of higher order and non-linear in nature. Both centralized and decentralized control systems using LACr are designed for the case studies. The performance of LACr controllers are tested with respect to servo changes using simulations. Decentralized LACr were found to be ineffective for non-linear MIMO processes, whereas centralized controllers yielded fruitful results.

Keywords: Control Applications, Robotics and Motion Control, Embedded Systems
Abstract: In many robotic applications and inertially stabilized electro-optic gimbal systems, precise positioning and speed control are highly important concepts. Due to size and weight limitations, motor is required to be small but torque density is desired to be high. In high torque and cheap PMSM and BLDC motors, cogging torque and friction are usually the challenging disturbance sources. In this study, cogging torque and friction are identified using position sensors which already exist in gimbal systems, so the cost of the system is not increased. Identified disturbances are rejected with feedforward algorithms. With only cogging torque compensation, 30-65% of the tracking error is removed in a feedback control system. The development of a controller design for a low-cost gimbal motor is presented and the following topics are discussed; accurate frequency response function measurement of gimbal system, controller design with ``loop shaping'' method according to system requirements, output disturbance sensitivity analysis. We also examine the controller implementation issues in an embedded world with their practical considerations.

Keywords: Control Applications, Robotics and Motion Control, Robust Control
Abstract: Undesirable residual vibration in dynamical system always causes many problems in logistic industries. For instance, a quadrotor is now considered to be used to deliver cargos instead of road transportation. However, the swing of cargos during the transportation process always causes threat to its safety and stability. In this paper, the mathematical model of a quadrotor slung system is established, and a double closed-loop control strategy is proposed, together with an input shaping design, to reduce the swing angle of load and keep quadrotor stable simultaneously in the presence of system uncertainties. Some numerical simulations are presented and the results verify the effectiveness of the proposed method.

Keywords: Robotics and Motion Control, Optimal Control and Optimization, Cooperative Control
Abstract: In this paper, a motion generation method for transportation of a massive object by multiple humanoid robots is proposed. A joint trajectory (acceleration level) of the whole-body is determined by solving a quadratic programming problem with inequality/equality constraints. The proposed method was varified by performing dynamic simulations using a dynamic simulator OpenHRP. Frictional constraints between the supporting foot and the ground has generally been formulated by inequality equations approximating a friction cone by an inverted pyramid. However the inequality constraint cause high computational cost. Therefore, in this paper frictional force is estimated by assuming virtual large masses at the tip of the limbs. The motion generated by the proposed method satisfies the constraints such as prevention of slip of the feet of the robots and avoidance of excessive joint torque, and it was possible to move an object of 200 kg. The simulation results with the proposed method were compared with the results with a conventional PD controller in order to show the effectiveness of the proposed method.

Keywords: Robotics and Motion Control, Sensors and Senor Fusion, Signal Processing
Abstract: Motion capture systems are used to gauge the kinematic features of the motion in numerous fields of research. Despite superb accuracy performance, the commercial systems are costly and difficult to use. To solve these issues, Kinect has been proposed as a low-priced markerless motion capture sensor, and its accuracy has been assessed using previous motion capture systems. However, in many of these studies, the anatomical joint angles captured using the Kinect are compared to the 3D rotation angles reported by the gold standard motion capture systems. These incompatibilities in the determination of the human joint angles can lead to higher error estimation. To accomplish a valid accuracy evaluation of the Kinect, we applied the inverse kinematics techniques in both Vicon and Kinect version 2 skeleton models to estimate lower body joint angles. The proposed method enabled us to capture the pelvic, hip, and knee joint angles using a single Kinect camera during gait. Moreover, the dependency of the proposed method to the position of the Kinect and the speed of the moving subject was investigated. In this study, the captured data of the Vicon motion capture system were used as ground-truth to assess the accuracy of the Kinect data. The results indicate the capability of Kinect in capturing human joint angles and also an affordable motion capture system applied in robotics and biomechanics applications.

Keywords: Robust Control, Robotics and Motion Control, Control Applications
Abstract: Kumamoto castle was damaged by big earthquake disaster in 2016. We have studied to archive precise 3D-stone data of the stone walls called ISHIGAKI because it should be useful for the renovation and the collapsing analysis of ISHIGAKI. For an efficient renovation, 3D-stone data should be obtained in a short period, i.e. the stone is modeled when it moves to the carry truck by a crane. In this paper, the small control moment gyro (CMG) device is prototyped, and the robust controller with a model error compensator is applied to CMG. Furthermore, the effective archiving procedure of 3D-stone is evaluated by an experiment using the CMG device.

Keywords: Robust Control, Game Theory, Nonlinear Control
Abstract: This paper investigates the problem of infinite horizon open-loop dynamic games for interconnected positive nonlinear systems with H-infinity constraint. Both Nash equilibrium as a non-cooperative game and Pareto optimality as a cooperative game are discussed. The systems considered here are cooperative systems and the H-infinity control recovers the influence of modeling error caused by linearization. For this class of positive systems, the conditions that guarantee the existence of Nash equilibrium and Pareto optimality are derived. It is shown that these conditions can be formulated in terms of cross-coupled algebraic Riccati equations (CCAREs) and a linear matrix inequality (LMI). Finally, in order to show the effectiveness of the proposed strategy, a simple example is demonstrated.

Keywords: Control Applications, Observer Design, Robust Control
Abstract: The classical control techniques such as Proportional-Integral-Derivative (PID) deliver excellent quality in the system performances with healthy sensors and actuators. However when failures of sensors and actuators occur, the control over the system goes partially or completely astray. Hence, we explore a robust control technique for the standard laboratory setup of the Twin rotor MIMO System (TRMS) which is similar, up to some extent, to a helicopter in terms of the non-linearities as well as cross interactions. Control algorithms such as robust PID and Extended Kalman (EK) observer - based robust PID are simulated and the system performance is compared with and without sensor and actuator failures. The control efforts are also considered in the tuning process to make sure they fit within the motor capabilities. An observer incorporated along with robust control algorithm for smooth operation. The results show that the implemented robust control methods along with EK observer provide very good performance by offering graceful performance degradation when facing either actuator or sensor failures.

Keywords: Robust Control, Nonlinear Control, Autonomous Systems
Abstract: This paper presents a robust depth control design for a class of Autonomous Underwater Vehicles (AUVs). We consider a SISO system by extracting 3 DOF decoupled equations of motion from a nonlinear mathematical model. This system is then bifurcated into internal and external subsystems by transforming it into a normal form. For state-feedback design, where all external states are considered to be measurable, a robust control scheme based on Sliding Mode Control (SMC) is proposed. The proposed controller achieves semi-global asymptotic stability in the presence of AUV model uncertainties and=or parametric variations. The work is then extended to output feedback design by using observer design approach of Boker & Khalil of combining an Extended High-Gain Observer and an Extended Kalman Filter so as to estimate the whole state vector. Mathematical analysis shows that, under output feedback controller, trajectories of the closed-loop system approach the invariant manifold in finite time and stay there then after. A simulation example is included to show the efficacy of the proposed controller.

Keywords: Robust Control, Nonlinear Control, Robotics and Motion Control
Abstract: The paper presents the solution for the problem of non-singularities produce by conventional sliding mode control. The solution is implemented on the application of non-holonomic mobile robot(NMR) in term of trajectory tracking. To solve this problem a controller is formed based on non-singular terminal sliding mode controller (NTSMC), which drives the tracking error to zero in finite time. It not only ensures the tracking but also solve the non-singularity issues. The designed control scheme is then modified using High gain Observer. With the Lyapunov analysis, the stability of the proposed algorithm is verified. Simulated, graphs are shown to prove the efficacy of the proposed control scheme and also assure that the controller achieved the desired objectives.

Keywords: Robust Control, Observer Design
Abstract: Observer-based force compensations are applied in an assembly of magnetized components. In the target assembly, a ferromagnetic component approaches a magnetized component. Then they are pulled toward each other due to the attractive force between them. It sometimes causes overshoot and collision although the positioning control system is designed to have no overshoot without such a force. To solve this problem, force compensation is added to the position control. An actuator generates the force to cancel the attractive force based on the force estimated by an observer. Two observers are compared. One is disturbance observer and the other is full-order observer. Experimental results demonstrate that the usefulness of the observers to estimate the attractive force and the effectiveness of the force compensation.

Keywords: Adaptive Control and Tuning, Automotive Systems
Abstract: We propose a novel optimization control strategy that integrates model-based optimization and square-wave extremum seeking control to optimize the efficiency of permanent-magnet synchronous machines using direct torque control (PMSM-DTC). Ordinary efficiency-optimization methods tend to require a precise mathematical model of the PMSM for estimating the optimal stator flux. Model-based optimization can rapidly shift the optimal parameters when operating conditions change. However, deviation in the stator flux estimation will occur if variation of the internal parameters is considered. The strategy proposed in this paper allows rapid switching of optimal parameters, and uses square-wave extremum seeking control to offset the deviation in the stator flux estimation quickly. The latter control scheme, ensures that the system always runs near the optimal point.

Keywords: Automotive Systems, Transportation Systems, Sensors and Senor Fusion
Abstract: Many functionalities in modern production cars need information about environmental parameters like temperature, solar radiation or road friction coefficients. These information are used within different modules, e.g. in the driving and operating strategies or for range prediction. This implies that current and future values of these environmental parameters have to be known to obtain optimal and accurate results for the single modules. In general, the environmental parameters are measured by the vehicles themselves. By collecting the information of multiple connected vehicles in a database, it is possible to obtain spatial and temporal distributed measurements of the environmental parameters. Based on these measurements, a prediction procedure for the environmental parameters is developed to obtain future values of them at specific route sections and instants of time, which can be used in predictive operating strategies and for range prediction. For the forecasts of the environmental parameters, different machine learning algorithms are deployed and an algorithm for input selection is applied to reduce the model complexity. The proposed procedure is validated using recorded temperature measurements.

Keywords: Automotive Systems, Variable-Structure/Sliding-Mode Control, Control Applications
Abstract: The purpose of speed tracking control is to keep the engine speed at prescribed set-points in the presence of disturbances. Engine speed depends on the torque demand and load torque acting on the engine. In this article, speed tracking control technique is proposed for torque management of the gasoline engine. Smooth Super-Twisting Algorithm based control scheme is developed to robustly achieve the desired objective. Instead of Mean Value Engine Model, the novel First Principle based Engine Model is used to implement the proposed methodology. The injected fuel mass flow rate is employed as control input to track the engine speed. Simulation results demonstrated validity of the proposed control technique.

Keywords: Control Applications, Complex Systems and Networks, Automotive Systems
Abstract: In order to solve the solution to the coupled Sylvester-conjugate transpose matrix equations, we present an iterative algorithm based on the hierarchical identification principle. For any initial values, the iterative solution consistently converges to the exact solution. Moreover, for any initial matrices, sufficient conditions are derived by means of a real representation of a complex matrix. Finally, In order to illustrate the efficiency of the proposed algorithm, control application to iterative algorithm has been presented

Keywords: LMIs, Automotive Systems, Adaptive Control and Tuning
Abstract: A vibration-isolating bed for ambulances using an inerter is proposed. The structure of the bed is assumed to consist of a bottom frame fixed to the ambulance floor and an upper frame on which the stretcher is placed. Both are connected only by the suspension, including the inerter, in the vertical and horizontal directions. Because the inerter can reduce the gain of the high frequency band and delay the response, the time when maximum acceleration is applied to bed can be shortened by passively in a limited space in ambulance. We consider the bed as a plant and the suspension as a controller, and then formulate the state equation. To reduce the acceleration within the movable range of the bed, the controller that minimizes the norm of the transfer function from the external force acting on the bed to acceleration is calculated by bilinear matrix inequality. The transfer function determines the structure and coefficient of the suspension. The usefulness of the bed is verified by numerical simulations.

Keywords: Control Applications, Cooperative Control, Optimal Control and Optimization
Abstract: We consider a multi-agent system that consists of two group of agents: clients and routers. Clients move according to their own agenda. Routers' control policy needs to be designed to maintain communication connectivity for others. The control policy designed in this paper consists of periodically computing the routers' desired positions in a distributed manner and then steering the routers to those desired positions. First, desired positions of routers are determined by an optimization problem which minimizes the length of the longest edge connecting a client and a router on a tree corresponding to the communication relationship between all agents. Then, the routers are steered to those desired positions by motion control. Simulations are done assuming that all routers are quadrotor drones to illustrate the results.

Keywords: Control Applications, Energy Technology, Adaptive Control and Tuning
Abstract: This contribution presents the control of the collective pitch system of a 20 MW wind turbine by using a nonlinear PI control (NPI) approach. Due to the fact that the operating point changes with the wind speed and that the plant is nonlinear, the controller has to adjust its parameters. This is done by using a gain scheduling procedure with bumpless transfer combined with an active tower damping mechanisms in order to reduce the effects produced by the pitching activity. As simulation platform, the software FAST is used for the representation of the wind turbine. Simulation results show that the control system based on the NPI approach contributes to an improvement in the control performance of very large wind turbines.

Keywords: Energy Technology, Optimal Control and Optimization, Predictive Control
Abstract: The PV-wind-storage microgrid plays an important role in solving power supply problems. Energy storage system is an important element to be considered when the optimal economic operation of microgrid is carried out due to the high cost and discount. To achieve reliable and economic operations of a PV-wind-storage microgrid, the lifetime characteristics of energy storage system also need to be fully investigated. In this paper, low pass filter is used to determine the power of super capacitor and battery, than established battery life quantitative model according to the equivalent cycle life curve. Considering the lifetime characteristics of energy storage system, a optimization to minimize power generation cost and to maximize the useful life of hybrid energy storage system has been achieved via the particle swarm optimization (PSO). The simulation results show the proposed method can optimize the system operations and has good optimization results.

Keywords: Networked Control Systems, Optimal Control and Optimization
Abstract: In this paper, we investigate the security issue in wireless networked control systems, where the sensor uses the channel hopping scheme to transmit the measurement data to the controller over multiple channels attacked by an energyconstraint jammer launching periodic Denial-of-Service attacks. Assuming that the jammer can select randomly several channels to attack at each attack time, there exists a tradeoff between the number of attacks, meaning the amount of time expended launching the attack, and the number of channels selected at each attack time. According to this, a problem arises in how to select the number of channels at each attack time to maximize the attack effect measured by the cost function in linear quadratic Gaussian control. By formulating this problem as an integer programming problem, an optimal attack schedule is obtained using the selection of channel number (SCN) algorithm proposed in this paper. Numerical simulations verify the effectiveness of the SCN algorithm.

Keywords: Robotics and Motion Control, Control Applications
Abstract: This work approaches the control problem of wheel, foot and wheel-foot combined motion and driven of an electric wheel-foot robot based on the stewart structure. The autonomous control method is designed and the functions of each link are elaborated. The cooperative control of four wheels driven independently is studied to realize flexible wheel motion. The airframe smoothing control algorithm is studied to ensure the stability of the body during moving. The experimental results show that the electric parallel wheeled robot combines the advantages of wheel and foot motion. At the same time, it fully utilizes the multi-degree-of-freedom driving capability of the wheel-foot composite robot, which reflects the comprehensive advantages of motion.

Keywords: Multivariable Control, Optimal Control and Optimization
Abstract: The esterification in polymerization process is a key step in polyester fiber production. There are three optimization objectives in the process of esterification, including rate of esterification ( s E ), average molecular weight ( nM ) and diethylene glycol content percentage ( t W ), which are in conflict with each other. However, currently, in many literatures they all considered the two performance indicators which can not show entirely the process of PET polymerization process. Then, this paper firstly performs a three-objective optimization problem in esterification process with NSGA-III. Additional, the existing multi-objective algorithm NSGA-III can provide a set of solutions, but the diversity of the solutions is poor. Therefore, an improved NSGA-III algorithm, called PNSGA-III, is proposed in this paper to deal with the three-objective optimization problem in esterification process. The PNSGA-III has been compared with original NSGA-III on 9 widely used benchmark test problems WFG1 to WFG9, and the three-objective optimization problem in esterification process. Experimental results indicate that PNSGA-III outperforms NSGA-III in terms of IGD metric and HV metric. Meanwhile, PNSGA-III reaches a better diversity in the esterification problem.

Keywords: Optimal Control and Optimization
Abstract: Electric vehicles (EVs) depend solely on batteries, have limited range issue due to limited capacity of a battery. The range of an EV or discharging time of a battery can be extended by applying some sort of an optimization strategy to the system. In the recent past, eco-driving application compliant with an online requirement for an EV with battery's static model have been discussed. Eco-driving problem has been formulated as an optimal control problem with the aim to minimize vehicle consumption only. EVs need proper charging station during a trip when the battery gets discharged beyond a certain limit and also when that limit is reached, discharging process gets fast as well. Some indication to the driver may increase battery's life as well, which gets reduced by many factors including complete charging and discharging. This paper addresses an energy management problem for an EV with battery's dynamic model. The results have shown that energy consumption has reduced approximately 8% using constrained case. The focus of this work is not only to minimize the vehicle power consumption using velocity profile approach but also to analyze battery's state during a trip. Optimized vehicle's velocity profile with fixed final position and an optimal torque profile and a resultant constrained battery's state of charge (SOC) will give better understanding and analysis of the proposed work.

Keywords: Optimal Control and Optimization, Aerospace Engineering
Abstract: L₁ navigation is widely used for path following. In this paper, it is applied to the heading control and coordination in constrained scenarios. A dedicated optimization framework, which can be tailored to requirements of different problems, is developed to determine the optimal L₁ subject to geometric constraints, control inequality constraints and other constraints on demand. Moreover, the cost function may be designed flexibly. In the first application study, the time- and energy-minimum cost functions are selected respectively in the two cases of the unmanned aerial vehicle (UAV) runway alignment. In addition, the proposed framework is capable of determining proper L₁ values for coordinative guidance, and it is validated in the second application study where multiple UAVs achieve a simultaneous alignment. Simulation results demonstrate the applicability and effectiveness of the proposed framework.

Keywords: Optimal Control and Optimization, Autonomous Systems, Control Applications
Abstract: In general, the automatic excavator can provide the high productivity output and also increase the safety factor by reducing the risk of the operations at the mine sites at the same time. In this paper, we focus on the loading operation of an excavator to the vessels on the dump trucks. Firstly, we use an optimization method that can decide the best deposit positions on the vessel. By depositing the soil following the positions above, the error between the desired soil shape and the deposited soil shape is decreased. Moreover, we adopt an iterative learning control (ILC) method in order to improve the error of the soil shape by taking an advantage of the repetative nature of the loading operation. Finally, we demonstrate both simulation and experiment results which show that the deposited soil shape efficiently approaches to the desired shape.

Keywords: Optimal Control and Optimization, Decentralized Control, Transportation Systems
Abstract: This paper deals with electric vehicle(EV) charging problem in a distributed method. EV is getting more attention from the environmental view point. For example, it can reduce oil consumption and emission of greenhouse gas. However EV needs much electric power for one charge, so electric power demand is forcasted to increase dramatically. And considered from the grid side, if power demand increases, voltage drop in the grid will be caused. So this paper proposes effective charging strategy for both each EV and gridside based on distributed control. The charging strategy we propose is minimizing base load generator cost and voltage drop at the same time to avoid concentration of the power use in a peak hour. At the last of this paper, the superiority of our method is demonstrated in simulations by using IEEE 15-bus system and describe about the future works.

Keywords: Optimal Control and Optimization, Energy Technology
Abstract: The high level energy consumption of the building sector is a common challenge faced by human beings. Building retrofit is an effective way to reduce the energy consumption of the building sector. To further promote the transition to a green building sector, many green building policies in the world are published. However, making a systematic whole-building retrofit plan optimally considering green building policies requires to solve a nonlinear mixed integer optimization problem. In addition, the whole-building retrofit project must be supported by a comprehensive and expensive energy audit. This study proposes a simplified optimization method for whole-building retrofit considering energy performance certificate standard (EPC) based on a grouping method and notch test data, aiming at reducing the complexity of solving the optimization problem and eliminating the need of a detailed energy audit. A case study shows that 74% energy savings and an A rating from the EPC rating system can be achieved with a payback period of 51 months.

Keywords: Adaptive Control and Tuning, Identification and Estimation, Optimal Control and Optimization
Abstract: The paper proposes an iterative PID gain tunin method using a cost criterion representing the vairance of the process output for the oritinal control objective. The approach estimates the gradient vector of the original cost criterion using closed-loop regulatory data. The PID gains are iteratively updated based on the gradient descent algorithm. Unlike the conventional IFT(IterativeFeedback Tuning), the proposed approach firstly estimates the sensitivity function from the regulatory control data for deriving the gradient of the cost function. While the IFT that requires a special experiment that feeds the closed loop response data as a reference signal, the proposed one estimates the gradient vector of the cost criterion without using a special experiment. Additionally, the proposed approach uses two sets of output data are collected from the closed-loop system, then each time series model is estimated from time-series analysis. The gradient of the output variance is estimated from the ratio of these derived time series models. The effectiveness of the proposed method is shown through a numerical example.

Keywords: Adaptive Control and Tuning, Multivariable Control, Sampled-data control
Abstract: In this paper, an adaptive output feedback control system design scheme with an adaptive parallel feedforward compensator (PFC) for discrete-time multi-input multi-output (MIMO) systems. In the proposed method, the parameters of a statical PFC without dynamics are adaptively adjusted so as to render the augmented system with an adaptive PFC almost strictly positive real (ASPR). Therefore, even if the controlled system changes during the operation, the ASPR based adaptive output feedback control can maintain the stability. The stability of the obtained adaptive control system is analyzed theoretically and the effectiveness of the proposed method is confirmed through numerical simulations on a combustion control of a diesel engine.

Keywords: Adaptive Control and Tuning, Nonlinear Control
Abstract: It is well known that backstepping method suffer from the problem of ``explosion of terms'', which is solved by dynamic surface control (DSC) method. This paper presents an improved dynamic surface control (IDSC) method, which constructs the state errors by directly using virtual control signals, rather than using the signals produced by first-order filters in DSC method. The signals produced by first-order filters will only be used to construct the virtual control laws and actual control law. This modification makes the state errors to be more free from the influence of first-order filters. The stability of systems controlled by the proposed IDSC method is proved based on Lyapunov theorem. Finally, the advantage of IDSC method has been shown by simulation results, and it can be seen in the simulation results that IDSC method has better tracking performance and is more stable than DSC method.

Keywords: Adaptive Control and Tuning, Nonlinear Control, Networked Control Systems
Abstract: The problem of controlling an uncertain nonlinear system with a saturated input quantization has not yet been addressed until now due to the difficulty of handling saturation and quantization simultaneously, although this problem is recognized as being of both theoretical and practical importance in the field of quantized control. In this note, we fill in the gap by presenting a new tuning function control scheme that is developed from a backstepping recursive design with n+1 steps. Moreover, a series of smooth functions are incorporated in our design to circumvent the possible chattering arising from the quantization error and saturation approximate error. It is proved that the steady-state tracking error is controlled into a prescribed bound with our scheme, and the global boundedness of all the closed-loop signals is ensured. Besides asymptotic performance, an explicit transient tracking performance in terms of L2-norm is also established.

Keywords: Adaptive Control and Tuning, Optimal Control and Optimization
Abstract: In this paper, a method utilized to control the structural response of an offshore wind turbine is proposed. This method used a device called the Dynamic Mass Damper attached to the top of the nacelle to control the structural response of the tower part. The model which is employed to analyze is a NREL 5 MW offshore wind turbine introduced by J. Jonkman et al. (2009) and the monopile foundation of this structure is considered with soil-component. The structural control is focus on dual directions, namely the fore-aft direction and lateral direction under seismic loading excitation. In addition, Genetic Algorithm (GA) is also applied to find out the optimized parameters to show the effectiveness of this structural control system. A MATLAB software code has been developed to compute and simulate this system. The results of simulation showed the effectiveness of this structural control method.

Keywords: Adaptive Control and Tuning, Sampled-data control, Sensors and Senor Fusion
Abstract: In this paper, a moving object tracking problem for quadrotor systems, containing model uncertainties, flying in dynamical and more practical environments such as GPS-denied and wind disturbance, is considered. For moving object detection, the maximum likelihood method is adopted to recover the trajectory of it based on features obtained from ORB-SLAM. For flight control, a discrete-time adaptive control method is developed in two steps. In the first step, the sliding mode and discrete-time adaptive control are adopted to overcome model uncertainties and plan desired attitudes. In the second step, the discrete-time adaptive control is adopted to overcome model uncertainties and external disturbance, and to track desired positions. The results are verified by two experiments. In the first experiment, a quadrotor with external 20 grams disturbance is used to track a 100 centimeter sinusoidal wave, and then the root mean square (RMSE) is calculated for comparison: by proposed method, the RMSE is 40 centimeter; by robust backstepping sliding mode control, 127 centimeters. In the second experiment, the mean error of human detection for a person standing 700 centimeters in front of a quadrotor is 40 centimeters.