Keywords: Autonomous Systems, Cooperative Control, Fault Detection
Abstract: In this paper, a decentralized adaptive fault-tolerant cooperative control scheme is proposed to achieve the attitude synchronization tracking control of multiple unmanned aerial vehicles (multi-UAVs) with actuator faults and directed communication topology. To alleviate the adverse effects caused by the in-flight actuator faults, adaptive laws are constructed and integrated into the developed attitude synchronization control scheme to enhance the formation flight safety by using the neural networks. The distinctive feature of the proposed method is to address the fault-tolerant attitude synchronization tracking control problem in a decentralized framework with directed communications. It is shown that the tracking and synchronization of multi-UAVs with respect to the desired attitudes can be achieved. Simulation results are provided to demonstrate the effectiveness of the proposed control approach.

Keywords: Adaptive Control and Tuning, Linear Systems, Multivariable Control
Abstract: This paper develops a new adaptive compensation control scheme for multivariable systems with some unknown actuator failures, based on a multiple-model approach. Such a scheme can handle possible structure changes from actuator failures and uncertain parameter changes of multivariable systems. Multiple parametrizations are employed for the system under different actuator failure patterns, each for design of an adaptive compensation control signal for individual pattern. Performance indexes are formed based on normalized estimation errors for all failure patterns and the control signal with best performance is decided to control the system. Such an adaptive multiple-mode design has the potential of fast failure compensation an uncertainty adaptation. A complete design procedure is presented, with clarification and analysis of some key system properties. Simulation results are used to demonstrate the effectiveness of the designed adaptive failure compensation control scheme.

Keywords: Networked Control Systems, Fault Detection, Linear Systems
Abstract: In this paper, we consider the detection problems of controller-to-actuator replay attacks in cyber-physical systems (CPSs). The replay attacks on controller-to-actuator channels can influence the dynamic property and even stability. For detection task, we employ a pseudo-random signal to code those communication channels, and code-decode abnormalities are generated when replay attacks occur. The abnormalities can be modeled as an additional Gaussian signal in system dynamic. Then, an anomaly detector is designed with a new type, which combines the pseudo-random signal covariance, to detect the abnormalities caused by replay attack and pseudo-random code. As a result, we obtain an optimal problem to solve the threshold value of detector and covariance of code signal. Finally, a numerical example is proposed to illustrate the effectiveness of the above conclusions.

Keywords: Fault Detection, Process and Chemical Systems
Abstract: Deep learning models have been proved to outperform shallow methods for industrial process fault detection because of their high capacity for complex nonlinearity. However, typical deep models applied to monitoring processes are conducted in a deterministic manner. They are unable to provide a confidence level for each decision. Also, most deep learning methods often need to integrate prior conditions, such as orthogonal latent variables, constraints, and some given distributions. The consequences of these issues cause lots of trials and errors as conventional deep models are built based on experiences. In this paper, a variational auto-encoder is used to set up a framework to tackle these problems. The learned latent variables, which would be orthogonal to each other, are constrained under the specified and optimized objective. Simultaneously, considering uncertainty in data, probability density estimates of latent variables and residuals instead of point estimates are given to design distribution based monitoring indices. A numerical example validates the effectiveness of the proposed method.

Keywords: Game Theory, Optimal Control and Optimization, Nonlinear Control
Abstract: In this paper, an optimal fault-tolerant control (FTC) method using zero-sum differential game is proposed for a class of nonlinear systems with actuator faults. Firstly, the FTC problem is transformed into a zero-sum differential game problem, in which the controller and the fault are regarded as two opposite players. Secondly, the Nash-equilibrium is established by minimax principle, and the coupling Hamilton-Jacobi-Isaacs (HJI) equation is further solved by adaptive dynamic programming (ADP) method. Besides, a FTC learning law of the weight is proposed, which only uses a critic network. Finally, an example is given to illustrate the efficiency and applicability of the obtained theoretical results.

Keywords: Cooperative Control, Decentralized Control
Abstract: At present, UAV is common to use as a single unit, but by operating as multi-agent system, it is possible to possibly be able to carry out more sophisticated tasks. For example, it may be possible to realize long-distance transportation with heavy object which impossible by a single drone. Therefore, we propose a control method to achieve cooperative transport so that the payload follows the virtual leader. We assume the problem of transporting objects by multiple quad-rotors using flexible cable. In this research, we treat Quad-rotor as a model regarded as a linear secondary system and use method of formation by consensus algorithm. Verify its usefulness by simulation experiment.

Keywords: Nonlinear Control, Complex Systems and Networks
Abstract: We study the behavior of a control-affine nonlinear system under periodic switching of the active control channel. The periodic switching between the control channels is implemented in a round-robin fashion. We claim that if a globally exponentially stabilizing feedback controller is sparsified using the round-robin scheme and the control action to each channel is scaled appropriately, then the resulting system is stabilized. For numerical proof of concept, we illustrate the effect of such sparsification on a linearized model of a coupled inverted pendulum - simple harmonic oscillator system.

Keywords: Linear Systems, Optimal Control and Optimization, System Theory
Abstract: In this paper, we give a state-space realization that maximizes the measure of quality (MoQ). MoQ of a system is a performance index that determines how easily the system can be controlled, while the standard notion of reachability/controllability just gives a binary information, e.g. reachable or unreachable. In this paper, we formulate the realization problem as maximization of the MoQ by a coordinate transformation matrix. We show that the optimal coordinate transformation is given in a closed form. The proposed method is effective in particular for preconditioning of an ill-conditioned grammian, the inverse of which is used to solve the reachability problem. We show a numerical example to illustrate this merit of the proposed method.

Keywords: Petri Nets, Embedded Systems, Discrete Event Systems
Abstract: In program update of control systems such as the function update, a span of update influence is not always clear in advance even if the number of LOC (lines of code) is small. We want to avoid unexpected behaviors; it is desirable to check rapidly whether the updated code does not affect the I/O processes which are not scheduled for update (non-updated I/O processes). Motivated by this, this paper proposes a program verification method for a safety update of the embedded system. Especially, we consider a program safety update problem to check whether the control flow of the updated code does not include that of the non-updated I/O processes or not. The proposed method focuses on the periodic characteristic of I/O processes to extract control flow of I/O processes. We use periodic inputs of the controllable and observable subsystem. To verify the validity of the proposed method, this paper considers an autonomous mobile robot.

Keywords: Networked Control Systems
Abstract: In this paper, data aggregation laws and distributed observers over delayed sensor networks with cyclic structures are proposed. In the proposed method, each node compensates communication delays of received data. For the delay compensation, each node predicts the future output based on state space models. To stabilize the aggregation data in the cyclic structure, the received data are multiplied by weight coefficients before the aggregation. The stability condition of the weighted aggregation laws is expressed by a weighted adjacency matrix. The aggregated value of the measurements at each node is expressed by a linear time-varying function of the current state. To estimate the state, we utilize the Kalman filters as the distributed observers. The effectiveness of the proposed method is confirmed by a numerical simulation.

Keywords: Networked Control Systems
Abstract: Event-triggered control is a method that the control input is updated only when a certain condition is satisfied (i.e., an event occurs). In this paper, event-triggered control over sensor networks, i.e., decentralized event-triggered control is studied based on the notion of uniformly ultimate boundedness. In uniformly ultimate boundedness, it is guaranteed that if the state reaches a certain set containing the origin, the state stays within this set. Using this notion, the occurrence of events in the neighborhood of the origin is inhibited. First, the design problem of decentralized event-triggered control is formulated. Next, this problem is reduced to an LMI (linear matrix inequality) optimization problem. Finally, the effectiveness of the proposed method is presented by a numerical example.

Keywords: AI and Expert Systems, Autonomous Systems, Fault Detection
Abstract: Classification of steel surface defects in the steel making industry is essential for the detection of defects through the classification of defects and for the analysis of causes that make defects. This makes it possible to reduce the defect rate of the product and drastically reduces the mass defect in the steel making process. Recently, Deep Learning has been used for defect detection using Convolutional Neural Network(CNN). Compared to the existing rule-based method, the defect classification using CNN achieved high performance. However, learning CNN requires hundreds or thousands of images. In the case of a defective image, it is difficult to obtain images of thousands or hundreds of images. To overcome these shortcomings, few-shot learning which need few images to train network can be used. Siamese Neural Network using CNN is used for few-shot learning. In this paper, we use few-shot learning with Siamese Neural Network using CNN structure with the contrastive loss to classify defects with a small number of steel surface defect images.

Keywords: AI and Expert Systems, Behavioural Systems, Human-Machine Systems
Abstract: Given emotion is important in maintaining mental and physical well-being. A multi-objective weighted reinforcement learning (MOW-RL) decision method is proposed to execute emotion regulation in human-robot interaction. The goal of emotion regulation is to minimize the cost of executing the service while eliminate user’s negative emotions or maintain user’s positive emotions. Considering the coordination problem of two objectives, fuzzy analytic hierarchy process (FAHP) is used to calculate the weight of each target reward and punishment function under different conditions. In addition, the influence factors of different personality on the difficulty level of emotion transfer are calculated by FAHP. Experiments are performed by 20 experimenters in the laboratory scenario, from which the results show that experimenters’ satisfaction is 2.3, which is close to satisfaction.

Keywords: AI and Expert Systems, Biological Systems, Intelligent and Learning Control
Abstract: Bat algorithm (BA) is a novel swarm intelligence optimization algorithm inspired by the behavior of bat hunting for prey and has been applied in many optimization problems. However, BA has some shortcomings including easy to fall into local optima and low precision of solution when solving some complex problem. In order to enhance its performance, a multisubpopulation bat optimization algorithm (MSPBA) is proposed in this paper. The specific idea of bat algorithm improvement is to divide the population into three subgroups, each using different search strategies. The first subgroup mainly performs global search to improve the global exploration ability of the algorithm. The second subgroup mainly performs local search to improve the accuracy of the algorithm. The third subgroup is mainly to enhance population diversity and avoid falling into local optimum. 10 standard benchmark functions are used to illustrate the performance of the proposed algorithm by comparing with DBA, BA, PSO, DE and CS. The simulation results show the superiority of MSPBA.

Keywords: AI and Expert Systems, Computational Intelligence, Fault Detection
Abstract: By leveraging the modern machine learning algorithms, we can build up more Artificial Intelligence (AI) systems, like self-driving cars, smart factories and financial analysis systems, to improve our daily life. In addition to building up an AI system, several prerequisites are required to drive the system, including data collection, data storage, machine learning models, training dataset, parameters tuning, and so on. To obtain the benefit of scalability and flexibility, most AI systems are built on a cloud platform, which shares resources with others in the same infrastructure. Though the above concept is trivial, the implementation faces big challenges when realizing it. In this paper, an easy-to-use cloud framework for machine learning as well as its implementation guideline is presented for building up a cloud-based development platform. We conduct several experiments on analyzing and monitoring the health condition of bearings of motors. We compare and analyze the feasibility of the proposed framework.

Keywords: AI and Expert Systems, Intelligent and Learning Control, Nonlinear Control
Abstract: Reinforcement learning (RL) methods enable us to construct a learning controller that acquires an appropriate control law tailored to a controlled object through trial and error (exploration). However, the exploring property is problematic when we apply RL methods to real world industrial problems, since it is difficult to maintain the control performance during learning. On the other hand, model-based control methods enable us to construct an appropriate controller based on known information (a mathematical model) of the controlled object. However, achievable control performance depends on the precision of the model and does not improve once the design procedure is finished. As a countermeasure for these issues, we introduce a notion of control approach “LeFiCo” in which a learning controller based on an RL method and a fixed (non-learning) controller are used simultaneously, and we propose a new method included in LeFiCo using an RL-based learning controller and a linear quadratic regulator in parallel for a class of nonlinear adaptive optimal control problems. We also conduct a theoretical analysis of this proposed method from the viewpoint of an RL problem, and we illustrate the validity and effectiveness of our method through numerical simulations.

Keywords: Variable-Structure/Sliding-Mode Control, Fuzzy Neural Systems, Adaptive Control and Tuning
Abstract: This article studies a novel adaptive Super-Twisting sliding mode control scheme for a micro gyroscope using fuzzy sliding gain. Firstly, an adaptive algorithm is used to estimate the unknown parameters of micro gyroscope. Secondly, this paper utilizes the universal approximation characteristic of the fuzzy system to estimate the gain of the Super –Twisting sliding mode controller and identify the gain of the controller on line, realizing the adaptive adjustment of the controller parameters. The simulation results verify the superiority of the proposed approach.

Keywords: Variable-Structure/Sliding-Mode Control, Networked Control Systems, Nonlinear Control
Abstract: This paper endeavors to investigate the sliding mode control (SMC) issue of the networked state-saturated systems. For the energy saving purpose in network communication, a dynamic event-triggering mechanism is employed to SMC design by introducing interval dynamic variable. With the aid of some appropriate Lyapunov functionals, the sufficient conditions are derived to ensure the asymptotic stability of the sliding mode dynamics and the reachability of the specified sliding surface. Moreover, a convex optimization algorithm is formulated to solve the dynamic event-triggering SMC law. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.

Keywords: Variable-Structure/Sliding-Mode Control, Nonlinear Control, Robust Control
Abstract: Sliding mode control (SMC) is a well-known nonlinear control method that is highly robustness against uncertainties such as disturbances, modeling errors, parameter variations. SMC has numerous successful applications in a wide range of fields of power electronics and motor drives, vehicle control, chemical plant control and so on. In particular, if an uncertainty satisfies a condition called the matching condition, then SMC becomes insensitive to the matched uncertainty. Here, ”matching condition” means the uncertainties are in the range space of the control input vector. However, conventional time invariant sliding surfaces cannot maintain a sliding mode for the duration of an excessive disturbance because of the control input saturation, even if the disturbance satisfies the matching condition. This paper describes SMC with a time-varying lemniscate-based sliding surface. When a sliding mode cannot be preserved because of an excessive disturbance, the proposed SMC moves the sliding surface closer to the current states by adjusting the vertical scaling parameter of the lemniscate-based sliding surface. The advantages of the proposed method are verified through simulation of a second-order time-variant linear system with bounded disturbances. Simulation results show that the robustness of the proposed method is improved compared to a conventional time-invariant method.

Keywords: Variable-Structure/Sliding-Mode Control, Process and Chemical Systems, Observer Design
Abstract: The continuous stirred tank reactor (CSTR) is epresentative of a typical class of chemical equipment where the dynamics is nonlinear. The problematic issues in control of a CSTR are the model uncertainties and external disturbances. Driven by these challenging problems coupled with the need for demanding levels of performance, this paper establishes the dynamic model of CSTR and then proposes an output feedback sliding mode control in light of the established model. The validity of the control algorithm and of the presented model are further verified by MATLAB simulation and experimental trials.

Keywords: Variable-Structure/Sliding-Mode Control, Switched Systems, LMIs
Abstract: This paper is concerned with the sliding mode control (SMC) problem for a class of uncertain switched systems subject to partly known sojourn probabilities. The sojourn probability is the probability of a switched system staying in each subsystem, which may be available or unavailable. A key issue is how to design a sliding mode controller to guarantee the stability performance of the reconstructed switched systems subject to the partly known sojourn probabilities. To this end, by using a set of stochastic variables, the switched system model is reconstructed with partly known sojourn probabilities. And then, the sliding mode control law is constructed to ensure the reachability of the designed common sliding surface. Furthermore, the sufficient condition is derived to ensure the mean-square stability of the closed-loop system under the partly unknown sojourn probabilities. Finally, a numerical example is provided to illustrate the proposed method.

Keywords: Aerospace Engineering, Identification and Estimation, Sensors and Senor Fusion
Abstract: Vision-based navigation can be classified primarily into relative and absolute navigation. Absolute navigation can estimate stable position solution of the vehicle. However, the performance of the feature detection algorithm greatly varies depending on the environment, and the matching accuracy may not be guaranteed. This represents navigation accuracy and can lead to serious navigation errors if an appropriate feature detector is not used. Therefore, in this study, we propose an algorithm that uses optimized feature information detector through frequency analysis of input image. To verify the performance of the proposed algorithm, we used the data obtained from flight experiments. The proposed algorithm prevents the occurrence of mismatching in flight experiments.

Keywords: Aerospace Engineering, Optimal Control and Optimization, Autonomous Systems
Abstract: The problem of powered landing guidance of reusable rockets is formulated in the presence of aerodynamic drag forces. This problem is challenging for onboard guidance applications due to non-convex thrust constraints and nonlinearities introduced by aerodynamic drag, mass-depletion dynamics, and free final time. Convex programming, which is characterized by deterministic convergence, is becoming increasingly attractive to address such problems. Lossless convexification is used to deal with the non-convex thrust constraints to make this guidance problem tractable. In addition, other non-convexities are eliminated by finite-element collocation based successive convexification, which relies on finite-element collocation discretization, linearization, trust region, and relaxation. Through successive convexification, a sequence of second-order cone programming problems is solved in each iteration by a state-of-the-art interior-point algorithm. The proposed approach is verified and compared with a direct method associated with an efficient nonlinear programming algorithm by numerical simulation.

Keywords: Autonomous Systems, Predictive Control, Aerospace Engineering
Abstract: This paper addresses the guidance & control problem of a single axis thruster configured spacecraft for autonomous and precise maneuvers in linear time-varying (LTV) systems. The autonomous control algorithms are particularly challenging due to the fact that crucial geometrical and physical constraints are coupled with the translational motion of the space vehicle, resulting in a complex motion planning problem. To this end, a novel feature of the proposed approach is the development of an inscribed polygon approximation (IPA) and convergence guaranteed G & C strategy where Quadratically Constrained Quadratic Programming (QCQP) problem is converted into Quadratic Programming (QP) problem within the Model Predictive Control (MPC) framework. The algorithm can address the three-body maneuver problem in circular and elliptic orbits by employing Clohessy-Wiltshire (CW) and Yamanaka-Ankersen (Y-A) relative motion dynamic equations. In addition, it is aimed to trace a fuel-optimal trajectory while ensuring safety with an explicit formulation of line-of-sight (LOS) constraints. Finally, the proposed strategy is compared with nonlinear input constrained QCQP and standard Taylor- Series expansion linearization results in terms of effective thrust region loss, fuel consumption, and real-time implementation capability via numerical simulations.

Keywords: Control Applications, Aerospace Engineering
Abstract: In this paper, attitude control for a flexible spacecraft with inertia uncertainty is considered. First, an adaptive law is constructed to estimate the upper bound of a certain function of the inertia uncertainty. Then, based on the proposed adaptive law, an adaptive sliding mode control law based on state feedback is established for the case while the flexible modal variables are measurable for the flexible spacecraft with inertia uncertainty. To verify the effectiveness of the presented control laws, simulations are performed in the presence of inertia uncertainty. Simulation results show that the proposed control laws attenuate the effect of inertia uncertainty successfully and the attitude states can be stabilized by the proposed sliding mode control laws.

Keywords: Nonlinear Control, Aerospace Engineering, Control Applications
Abstract: In this paper, attitude control reconfigurability of a four pyramid-type actuators spacecraft is analyzed in different actuator faults cases based on the controllability theory. The results of controllability and small-time local controllability (STLC) of the faulty system are proven by Lie algebra and convex theory. A time index is introduced based on the optimal control theory to evaluate the speed of reconfiguration in gimbal faults cases. The condition that guarantees the reconfigurability of the system is obtained with the controllability of the faulty system, which implies that the system is always reconfigurable when there is at least one actuator working even with partial failure. Moreover, our results indicate that the failures of rotors influence the STLC of the system while the failures of gimbals degrade the speed of attitude adjustment.

Keywords: Fault Detection, Aerospace Engineering, Signal Processing
Abstract: A novel second-order output spectrum (SOOS) based method with a local tuning approach (LTA) is proposed in this paper to locate multiple bolt-loosening faults in complex structures. This new method utilizes a more general multi-degree-of-freedom (MDOF) model with nonlinear components, due to not only bolt-loosening faults but also inherently existing nonlinearity in structures, to derive the SOOS of virtual beam-like structures. Through the LTA, one extra equation is given to derive the local SOOS based damage indicator. In numerical study, the results of comparison with previous methods demonstrate that the fault feature in proposed method provides more local characteristics. Hence, this novel SOOS based method gives more accurate information about the fault location and can be used as an effective and reliable nondestructive evaluation method (NDE) for the detection and location of multiple faults in complex structure with inherent nonlinearity.

Keywords: Fault Detection, Filtering
Abstract: In the traditional Multiple Model Adaptive Estimation (MMAE) algorithm, the extended Kalman filter has theoretical limitations, and the establishment of accurate aircraft mathematical model is almost impossible. In this paper, the Kernel Adaptive Filter (KAF) is introduced to replace the Kalman filter, a new multi-model adaptive estimation fault diagnosis method is proposed. Based on the kernel methods, the complex nonlinear system is mapped to the high-dimensional feature space, then the adaptive filter is designed in the high-dimensional feature space without the need to know the system model in advance. After training of KAF using the offline input control signal and output flight states measurement with noise, the estimation of real flight states values and actuator fault detection and isolation can be realized online. The simulation results show good performance of new fault diagnosis method in actuator fault diagnosis.

Keywords: Fault Detection, Intelligent and Learning Control, Process and Chemical Systems
Abstract: The kernel principal component analysis (KPCA) is widely applied in fault diagnosis of complex nonlinear chemical processes. However, the cumulative contribution rate which extracts the kernel principal is obtained based on the subjective judgment of expert opinions. Therefore, this paper presents a novel adaptive kernel principal component analysis (AKPCA) based on a moving window integrating the threshold method to adaptively extract the kernel principal. The covariance matrix is obtained based on the kernel function. Then the value of the covariance matrix is adaptively judged by using a moving window integrating the threshold to select the core principal component. Finally, the proposed method is applied in the fault diagnosis of the Tennessee Eastman (TE) process in complex chemical processes. Compared with the KPCA and the KPCA based on the threshold, the results verify that this proposed method can improve the cumulative contribution rate beyond 95%, which accurately find the main factor of the fault diagnosis in the complex chemical process.

Keywords: Fault Detection, Networked Control Systems, Linear Systems
Abstract: The networking of Cyber-Physical Systems (CPS) is a worldwide trend. With the growing accessibility of data vulnerability to attacks increases. Recent cyber attacks like Triton point out the necessity of new methods to secure CPS. Despite multiple dissimilarities between CPS and a general IT system, the mature field of IT security can inspire new methods for the protection of CPS such as cryptography. The encryption of signals transferring through the network is not new. The homomorphic encryption scheme allows execution of mathematical operations in the encrypted domain. This allows to encrypt not only signals in the network but also parameters and variables of a remote monitoring system. In this way, it is much more difficult for the adversary to gather information about the plant to execute a stealthy attack. In this paper, a somewhat homomorphic encryption scheme is applied to a CPS to ensure safe and secure communication by protecting all signals and variables in the network without loss of performance. A numerical example demonstrates the security level that can be achieved by applying this scheme.

Keywords: Fault Detection, Process and Chemical Systems, Large-scale Systems
Abstract: In this paper, a distributed fault detection scheme is proposed for plantwide chemical processes. First, a two-port system model structure for plantwide chemical processes is introduced. A distributed approach to fault detection for plantwide systems is developed, which deals with the interactions among process units. These detectors are designed to be sensitive to faults but insensitive disturbances. Based on the textit{vector dissipativity} condition, the gains of estimators and residue generator are determined offline by solving linear matrix inequalities. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.

Keywords: Manufacturing Systems, Networked Control Systems, Pattern Recognition
Abstract: The continued increase of devices as Internet of Things (IoT) at manufacturing facilities has been becoming popular even at small factories, and transformation toward less human resources have rapidly been increased. At the same time, it is causing threat on security and data falsifying. Recently, devices sharing and providing safe data including texts and images within a specific facility are the key for improvement of providing trusted products. In this paper we present new manufacturing line system including encryption data provision and apply this new method especially in food industry at IoT based on manufacturers compatible with small and medium businesses. This new technological method will improve product reliability and traceability accuracy of manufacturing process well-managed against customer satisfaction to the products.

Keywords: Pattern Recognition, AI and Expert Systems
Abstract: Convolutional Neural Networks (CNNs) are applied for hand gesture recognition and achieve desirable performance with homogeneous background scenes, however, there are still challenges for complex human-computer interaction scenes. In this paper, the gesture classification model with Two-Stream Convolutional Neural Networks (Two-Stream CNNs) is proposed. The Two-Stream CNNs structure is built which incorporates both natural light and depth images. And then the fusion features are extracted by convolutional kernels to achieve good performance. Finally, the combination of natural light and depth images is verified to improve the hand gesture recognition accuracy for complex backgrounds. The model is trained and tested on the standard dataset benchmark of American Sign Language (ASL), and experimental results prove that the model can achieve better classification performance on the dataset than the CNNs with single stream. The model improves the classification performance, by 0.9793 F1-score.

Keywords: Pattern Recognition, Computational Intelligence
Abstract: In view of the shortcomings of low-dimensional modeling, difficult to move and expensive, the 3D reconstruction system based on the improved ICP (Iterative Closest Point) algorithm is proposed. Firstly, the Kinect sensor is employed to collect color images and depth images, and the Kinect pose is calculated by tracking and acquiring image feature points, which effectively improves the camera's error in obtaining depth images and image noise, and realizes local map construction. Based on the ORB-SLAM (Simultaneous localization and mapping based on ORiented brief feature recognition) system, the key frames are extracted to obtain more image pixels. The improved ICP algorithm is presented to construct a three-dimensional dense map, and a good three-dimensional model of indoor scene is obtained. The experimental results show that the proposed high-resolution three-dimensional reconstruction system with large scenes has good stability, accuracy and robustness, and has great application value.

Keywords: Pattern Recognition, Robotics and Motion Control, Real-Time Systems
Abstract: The core technology to mobile robots is referred to as Simultaneous Localization and Mapping (SLAM). It has been wildly investigated on improving pose estimate accuracy and mapping consistency. This paper proposes several algorithms on the target. Firstly, a fuzzy frame evaluation and filtering strategy based on OpenCV is proposed to prevent fuzzy frames from affecting pose estimation. Secondly, a Bi-directional PnP-RANSAC method is proposed to estimate the pose between frames, in which more matching information is exploited, and the consistency of results can be verified to obtain more accurate pose results. Furthermore, for the consideration of efficiency, a GPU acceleration algorithm named SiftGPU[1] is applied in the feature extraction and matching process to improve real-time performance. Finally, a complete RGB-D SLAM framework is built to realize the above algorithms. Experiments have been conducted in the lab environment and dataset[2], producing global consistent dense 3D environment representation and accurately estimated trajectory, demonstrating the effectiveness of the proposed methods.

Keywords: Transportation Systems, Pattern Recognition, AI and Expert Systems
Abstract: Since more and more vehicles are used, traffic speed prediction is of great importance to ensure public safety and improve traffic management. However, it is difficult to provide an accurate traffic speed prediction since the traffic is affected by many complex factors, in which the most challenges are internal sequential correlations, spatial dependencies and external factors such like weather and holidays. To solve the problem, we propose a broad learning based method, named spatio-temporal broad learning networks (ST-BLN) to forecast the traffic speed. First, to deal with temporal dependencies and spatial dependencies of traffic speed, spatio-temporal region of support (ST-ROS) is defined and identified using historical data. More specifically, for spatial dependencies, we propose a method of slide-window correlation coefficient to find related road segments with the current one. And for temporal dependencies, we obtain temporal region of support by analyzing the cumulative distribution of correlation coefficient. Second, we extract slow features of traffic speed as feature nodes of ST-BLN which can carry major information provided by raw speed. Further, the feature nodes are combined with external factors to generate enhancement nodes. In order to prevent overfitting and eliminate the influence of noncritical information, we simultaneously add L1 regularization and L2 regularization to the solution of final connecting weights. Finally, the efficacy of the proposed method

Keywords: Human-Machine Systems, Signal Processing, Biological Systems
Abstract: This paper describes the development and working of our lab-developed fNIRS system which measures the concentration changes in oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) from the optical NIR light signals. 128 light emitting diodes (LEDs) with two wavelengths (735 nm and 850 nm) are used in our system. This NIR light intensity passes through the brain tissue, get absorbed and scattered, is received and measured by only single photodiode (PD) in order to compute the desired blood chromophores. The circuit for LEDs was designed using serial peripheral interface based output expansion chips. Wireless (Wi-Fi) technique was employed in the system to transfer the intensity values to our lab-developed software. The software has the capacity to show the data plots in real-time and can store the information for offline evaluation. The functionality of the system was tested using a phantom of known scattering and absorption coefficients. The LEDs and PD were placed on the phantom and different arrangements for light shooting were utilized to yield testing data. The tested system exhibited the measured intensities used to measure HbO and HbR changes in 128 channels.

Keywords: Automata, Discrete Event Systems, Networked Control Systems
Abstract: This paper investigates fault tolerant control of parallel interconnected asynchronous sequential machines (ASMs) via matrix approach. This problem is challenging since each submachine can fall into an unauthorized transition caused by adversarial inputs. By using the semi-tensor product method, the dynamics of parallel interconnected ASMs are depicted as a discrete-time algebraic form. Under this framework, reachability and fault detectabillity of parallel interconnected ASMs are discussed. Then, several necessary and sufficient conditions for checking reachability and fault detectabillity are obtained. Finally, for better presentation, a system composed by three asynchronous error accumulators in parallel connection is provided to illustrate the feasibility of the proposed method.

Keywords: Complex Systems and Networks, Biological Systems, Switched Systems
Abstract: This paper discusses the local convergence of Boolean networks (BNs) with state-based disturbances by pinning control. Under the framework of semi-tensor product (STP) of matrices, the algebraic expression of a Boolean network (BN) can be obtained. Then, the definition of local convergence is given. Note that compared with the stability, the time is limited to no more than n steps rather than 2^{n} when considering the local convergence. By analyzing the algebraic expression, some necessary and sufficient conditions for local convergence were derived. Moreover, how to choose the pinning-nodes was analyzed, and pinning controllers are designed such that systems with state-based disturbances achieve locally convergence. Finally, the proposed method is well demonstrated by some examples.

Keywords: Discrete Event Systems, Switched Systems, Identification and Estimation
Abstract: An estimation algorithm for selection probabilities of probabilistic Boolean networks is developed in this paper. A recursive estimation procedure is deduced from Bayes theorem and gives the expression of posterior probability distribution of the selection probabilities. To realize the distributions in the fashion of numerical computation, Markov chain Monte Carlo (MCMC) method with Metropolis-Hastings sampler is exploited and provides approximated probability densities. Some numerical examples are illustrated to demonstrate the effectiveness and computation complexity of the proposed framework.

Keywords: Linear Systems, Discrete Event Systems
Abstract: This note investigates the output regulation problem of Boolean control networks (BCNs) under event-triggered control via semi-tensor product of matrices. Firstly, based on an algebraic state-space representation of BCNs, a necessary and sufficient condition is obtained for the solvability of the output regulation problem. Secondly, the feedback event-triggered control laws are obtained by using augmented system approach. Finally, an example is given to illustrate the effectiveness of the proposed results.

Keywords: Networked Control Systems, Cooperative Control, Decentralized Control
Abstract: In this paper, the consensus problem of heterogenous multi-agent systems (MASs) based on decentralized event-triggered strategy is considered. In order to alleviate the energy consumption of updating the control input, a decentralized event-triggered scheme is adopted. Each agent can decide its own triggering time sequence which is based on the communicating information from its neighbors and update its control input agreement according to its corresponding event time. Based on Lyapunov functional method, Lasalle invariance principle and algebraic graph theory, some novel sufficient conditions are derived to ensure the consensus of heterogeneous MASs and the Zeno behavior is also demonstrated to be excluded. Numerical examples are finally presented to demonstrate the results of the decentralized event-triggered consensus control.

Keywords: Networked Control Systems, Linear Systems
Abstract: This paper addresses the problem of stability analysis of a linear distributed networked control system (DNCS) by employing the aperiodic sampled-data strategy. Firstly, a self-triggering strategy for each subsystem of a DNCS is developed in which the next sampling instant for control law update is computed. To further improve the efficiency of the utilization of resources of DNCS, a hybrid triggering mechanism combining event-triggered and self-triggered methodologies is proposed. The proposed hybrid approach comprises of two steps: Firstly, the next sampling instant is computed using self-triggered scheme for each subsystem of a DNCS and thereafter, the event-triggered condition is verified at these sampling instants. The control law for each subsystem is updated only if the event-triggering condition is violated. Each subsystem transmits its state information to the neighbouring subsystems only when its state error violates a predetermined event-triggering condition. This reduces the number of sampling instants as well as the control updates, thereby leading to reduction in the usage of network resources. The results proposed in the paper are validated using a simulation example.

Keywords: Fuzzy Neural Systems, Adaptive Control and Tuning, Nonlinear Control
Abstract: Cable driven parallel robots (CDPRs) has several advantages compared to conventional parallel robots such as large workspace, high dynamics, and high payload by virtue of elastic lightweight cable links instead of existing rigid links. However, the CDPRs have a force and position control problem caused by nonlinearity and uncertainty in pulleys and elastic cable. Especially, the friction between pulley and cable brought significant force differences between winch-motor and end- effector. In this paper, we present pulley friction compensation method to obtain direct tension at the end-effector without utilizing additional force sensor by using an Artificial Neural Network (ANN). The proposed control algorithm which is composed of a PID-controller to track the desired tension and an ANN to estimate un-modeled force differences between winch-motor tension and the end-effector tension. The ANN could estimate the friction in the pulley used for the CDPRs based on the measured datasets of the elastic cable for the given experimental condition. The experimental results showed that pulley friction could be remarkably compensated during single cable tension control.

Keywords: Intelligent and Learning Control, Fuzzy Neural Systems, Nonlinear Control
Abstract: The stability and stabilization are one of the important control problems. This paper proposes a new design method of a stabilizing controller for discrete-time Takagi-Sugeno fuzzy systems so that the controller stabilizes a larger class of nonlinear system than ever. Different from the conventional parallel distributed compensator(PDC), a novel non-PDC controller is introduced. The controller employs not only the current state variables of the system but also the past ones for the feedback. In order for the controller to be feasible for a wide class of nonlinear systems, a new Lyapunov function which has delayed variables as well is adopted to obtain asymptotic stability conditions of the fuzzy system with the controller. The relaxation lemmas are applied to make less conservate in such asymptotic stability conditions. In addition to the theoretical analysis and synthesis, examples are considered. The proposed design method and the existing ones are compared by such numerical examples to show the validity of the proposed control design procedures.

Keywords: Intelligent and Learning Control, Large-scale Systems, Robust Control
Abstract: This work focuses on adaptive neural dynamic surface control (DSC) for an extended class of nonlinear MIMO strict-feedback systems whose control gain functions are continuous and possibly unbounded. The method is based on introducing a compact set which is eventually proved to be an invariant set: thanks to this set, the restrictive assumption that the upper and lower bounds of control gain functions must be bounded is removed. This method substantially enlarges the class of systems for which DSC can be applied. By utilizing Lyapunov theorem and invariant set theory, it is rigorously proved that all signals in the closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB) and the output tracking errors converge to an arbitrarily small residual set. A simulation example is provided to demonstrate the effectiveness of the proposed approach.

Keywords: Intelligent and Learning Control, Nonlinear Control
Abstract: Usually, the convergence analysis of a continuous-time iterative learning control (ILC) algorithm is carried out by using either Contraction Mapping (CM) or Composite Energy Function (CEF) based methods. They are subtly different in terms of types of systems to which it can be applied, the convergence properties, convergence condition and the standing assumptions. Although the link between these two methods has been observed in literature, there is no clear statement to show that CEF-based method can be served as a unified analysis tool. This paper shows this fact by re-proving the well-known CM-based convergence results of P-type ILC algorithms using a novel CEF method.

Keywords: Intelligent and Learning Control, Observer Design, Nonlinear Control
Abstract: This paper focuses on detecting the forces exerted by stroke patients in the repetitive exercises using the rehabilitation robotic system: EMU, without using any external force sensor. As the model of such a system is hard to identify precisely, a simple feedback-based iterative learning control algorithm is proposed to identify such forces. The convergence analysis of such force estimators is provided in this paper. The simulation and experimental results illustrates the effectiveness of the proposed force observers.

Keywords: Aerospace Engineering, Variable-Structure/Sliding-Mode Control, System Theory
Abstract: In recent years, the use of VSCMG/IPACS for satellites has been considered, because Earth observation satellites are required to have agility and miniaturization. However, in previous research, the control laws of VSCMG/IPACS at large-angle and agile attitude maneuvers have not been studied. In this study, we clarify that it is impossible for conventional methods to achieve large-angle and agile attitude maneuvers, while accurately tracking power. Therefore, we propose a system capable of simultaneous satellite attitude control and power tracking with VSCMG during large-angle and agile attitude maneuvers. The proposed steering law switches the steering laws according to singularities, and enables the satellite to change attitude quickly without power errors. In addition, numerical simulations demonstrate through comparison that the proposed steering law is more useful than conventional steering laws.

Keywords: Control Applications, Variable-Structure/Sliding-Mode Control
Abstract: For both balanced and unbalanced grid voltage conditions, this paper proposes a direct power control (DPC) scheme for doubly fed induction generator (DFIG) based on second-order sliding mode super-twisting algorithm. Firstly, DFIG model under two-phase stator stationary reference frame is presented and power analysis for unbalanced grid condition is carried out in detail. Second-order sliding mode DPC strategy is then designed to improve dynamic performance and restrain current harmonic. System stability is analyzed based on Lyapunov method and control parameters ranges for finite time stability are accordingly deduced. For unbalanced grid voltage condition, while reserving the excellent control characteristics of second-order sliding mode DPC strategy, a flexible reference power compensation method is employed and the compensation values can be fast switched according to different operation objectives. Synchronous rotation coordinate transformation and current loop control are needless during the realization process of the proposed control strategy which simplifies the system structure. Simulation results for a 2.0MW DFIG verify the effectiveness of the proposed super-twisting algorithm based DPC scheme for both balanced and unbalanced grid voltage conditions.

Keywords: Power Systems and Power Electronics, Variable-Structure/Sliding-Mode Control, Delay systems
Abstract: This paper considers the growing effect of reheat turbine delays in a power system with multiple delays and nonlinear disturbance. In order to improve the operating condition of the systems and avoid the destabilizing effects of time delay, a model representation for reheat turbine delay is developed where a multiple delayed nonlinear term is used to describe disturbances. On this basis, an admissible upper bound is provided based on the Lyapunov Razumikhin theorem and an acceptable ultimate bound is calculated for the load disturbance. An improved load frequency sliding mode control (SMC) is synthesized such that the controlled system is uniformly untimately stable even in the presence of time delays and nonlinear disturbances. Effectiveness of the proposed method is tested by simulation via an isolated power system supplying a service load.

Keywords: Variable-Structure/Sliding-Mode Control
Abstract: This paper has investigated the second-order sliding mode (SOSM) controller design problem for a class of sliding mode dynamics with an upper-triangular perturbation. The construction of this SOSM controller consists of two steps. The first step is to use the adding a power integrator technique to construct a Lyapunov-based controller which locally finitetime stabilizes the sliding variables. The other step is to impose a kind of nested saturation function on the Lyapunov-based controller and adjust the saturation level to ensure the global convergence of sliding variables. Lyapunov analysis has been utilized to test the finite-time stability of the closed-loop system. Simulation results are given to verify the effectiveness of the proposed method.

Keywords: Variable-Structure/Sliding-Mode Control, Control Applications, Robotics and Motion Control
Abstract: The modeling and controller design for a tilt tri-rotor unmanned aerial vehicle (UAV) is studied in this paper. Taking the advantages of high cruise efficiency and vertical takeoff and landing(VTOL) capability, the tilt tri-rotor UAV attracts many researchers' attentions. However, due to the existing nonlinearities and time-varying structure, research on the modeling and control scheme of the tilt tri-rotor UAV is still a challenging work. To achieve stable flight control in helicopter mode, a robust and efficient controller is developed. First, the aircraft is described in detail, and the mathematical model of the aircraft is obtained. Then, a control allocation algorithm is developed to improve modeling precision. Finally, a hierarchical flight controller is designed by combining the theory of dynamic inversion and sliding mode control. The results show that the proposed control scheme can achieve strong robustness.

Keywords: Control Applications, Aerospace Engineering
Abstract: An autonomous flight control method of UAV(: Unmanned Aerial Vehicle) which flying just above the UGV(: Unmanned Ground Vehicle) using only the information of the onboard camera of UAV is proposed in this paper. In the conventional research, motion capture system, GPS, and laser range finder are often used to measure the position and posture of UAV. Although the conventional measurement system is effective in certain conditions and limited environments, it is not always applicable outdoors. There have also been researched about the cooperation of UAV and UVG, but much previous research, its effectiveness was shown in by numerical simulation or experiments using many sensors. By using our proposed system, it is possible to select a feature point on the UGV placed on the ground by using only a UAV onboard camera, and to fly the UAV just above the UGV. Besides, if the UGV moves along a trajectory, UAV can fly above the UGV. In our experiment, we use ROS for an image processing and calculation of control inputs. To operate the UAV automatically, we apply a simple PID control method and its gains are individually adjusted. The effectiveness of our proposed method is shown by experiment.

Keywords: Nonlinear Control, Aerospace Engineering, Control Applications
Abstract: A robust nonlinear output feedback control method is presented, which achieves two degrees of freedom (2-DOF) attitude tracking of a helicopter system test bed. The control law is designed to compensate for uncertainty in the helicopter system dynamic model, including input-multiplicative parametric uncertainty. To reduce the computational requirement in the closed-loop system, constant feedforward estimates of the input-multiplicative uncertainty are utilized in lieu of adaptive parameter estimates. Eschewing the high-gain feedback requirement that is characteristic of standard sliding mode observer methods, the proposed control method utilizes a bank of dynamic filters, which operates as a velocity estimator in the closed-loop system. Computer simulation and experimental results are provided to demonstrate the performance of the attitude tracking control method using the Quanser 2-DOF AERO helicopter.

Keywords: Nonlinear Control, Control Applications, Aerospace Engineering
Abstract: A robust nonlinear output feedback control method is presented, which achieves asymptotic position and attitude regulation for a six degrees of freedom (6-DOF) quadrotor unmanned aerial vehicle (UAV). The control law is designed to compensate for uncertainty in the quadrotor system dynamic model, including input-multiplicative parametric uncertainty. To reduce the computational requirement in the closed-loop system, constant feedforward estimates of the input-multiplicative uncertainty are utilized in lieu of adaptive parameter estimates. In order to avoid the high-gain feedback requirement that is characteristic of standard sliding mode observer methods, the proposed control method utilizes a bank of dynamic filters, which operates as a velocity estimator in the closed-loop system. Simulation results are provided to demonstrate the performance of the attitude control method using the Quanser 6-DOF QBall 2 system test bed.

Keywords: Nonlinear Control, Control Applications, Aerospace Engineering
Abstract: This paper studies event-based attitude regulation design for uncertain rigid spacecraft in terms of Rodriguez parameters. First, an input-to-state stabilizing controller is developed to quantify the effect of sampling error, allowing us to embed an event-triggering mechanism by virtue of this specific external stability. It leads to a valid event-triggered controller for attitude regulation with any specified precision.A numerical example is presented to illustrate the effectiveness of the proposed algorithm.

Keywords: Adaptive Control and Tuning, Transportation Systems, Fault Detection
Abstract: This paper studies the adaptive fault-tolerant tracking control problem for the high-speed trains with intercar flexible link and traction actuator failures. This study is focused on a benchmark model which, as a main dynamic unit of the CRH (China Railway High-speed) train, is a two-car dynamic system with a flexible link between two cars, for which the input acts on the second car and the output is the speed of the first car. This model is under parameter uncertainties and subject to uncertain actuator failures. For such an underactuated system, to ensure the first car tracking a desired speed trajectory, a coordinate transformation method is employed to decompose the system model into a control dynamics subsystem and a zero dynamics subsystem. Stability analysis is conducted to show that such a zero dynamic system is Lyapunov stable and is partially input-to-state stable. An adaptive fault-tolerant controller is developed which is able to ensure the closed-loop system signal boundedness and desired speed tracking, in the presence of the actuator failures and unknown system parameters. Simulation results from a realistic train dynamic model are presented to verify the effectiveness of the adaptive controller.

Keywords: Fault Detection, Process and Chemical Systems, Manufacturing Systems
Abstract: Close-loop control is widely used in modern industrial process which brings obvious process dynamics. Disturbances on process may be compensated by the control actions, and thus it may have little influence on the process performance like product quality and waste discharge. Therefore, process monitoring without considering the influences of process variations on performance and process dynamics may cause nuisance alarms. To achieve comprehensive process monitoring, performance-relevant full decomposition of slow feature analysis is proposed for processes under closed-loop control by simultaneously considering the influences of process variations and control actions on performance and process dynamics. First, the proposed algorithm extracts variations directly relevant to performance variables using canonical correlation analysis. In this way, process variable space can be decomposed into performance-relevant subspace and process-relevant subspace. Next, static and dynamic variations of each subspace are extracted to distinguish operating condition deviations from real faults. The proposed monitoring structure offers a fine-scale decomposition of process variations, and also achieves comprehensive process monitoring of process static and dynamic characteristics. Besides, it can effectively indicate whether a disturbance influences the performance and process dynamics. Finally, the proposed method is applied to the Tennessee Eastman process.

Keywords: Fault Detection, Switched Systems, Identification and Estimation
Abstract: This paper addresses the supervisory fault tolerant control issue of a class of semilinear wave equations in the presence of process and boundary faults ranging over a finite cover. By using a supervisory switching law among a family of candidate controllers being associated with the fault set, every fault set can be accommodated by one controller and the states of system are guaranteed to converge to a bounded region.

Keywords: Observer Design, Fault Detection, Distributed Parameter Systems
Abstract: In this paper, a distributed secure state estimation problem that is state estimation in distributed observer system in the presence of attacks is mainly discussed. We introduce the notion of graphical distributed observer representation, virtual state, and a subgroup to derive a condition for the secure state estimation. By the graphical distributed observer representation, we can consider the system's observability and communication pass connectivity as graph connectivity at once. With the notion of virtual state and the subgroup, we derive the state estimation condition which is the inequality relationship between the number of the allowable attacked observer and the graph connectivity as a theorem. Finally, a simple numerical example verifies the theorem.