Keywords: Robotics, Motion Control, Learning Systems
Abstract: Dynamic Movement Primitives (DMPs) is a general method for learning skills from demonstrations. Most previous research on DMP has focused on point to point skill learning and training, and the skills learned are usually generalized based on the same tool or manipulator. There is rare research on skill learning and transfer between two or more different tools. For this problem, a new DMP-based skill learning and transfer framework is proposed for the use of multiple tools. It consists of two types of skills: Object Effective (OE) skills and State Switching (SS) skills. OE skills consider the tools' limited forcing areas that can be expressed as constrained inequalities, and extract skills from demonstrations. It can then be generalized along with changes in the shape and range of influence of a new tool. SS skill is used to connect OE skills and implement changes of contact points of the object and tool. Finally, the two skills are integrated and used to realize the transfer of skills from the demonstrated tool to the new tool. An experiment is conducted to verify the effectiveness of the proposed framework, and the procedural solutions and the final manipulation effect are shown in detail.

Keywords: Intelligent and AI Based Control, Robotics, Sensor/Data Fusion
Abstract: YOLO (You-Only-Look-Once) is a deep learning-based one-stage detection method that has been widely used and achieved great success in image classification and localization. As the state-of-the-art method, YOLO has been upgraded to version 5. This paper proposes a new approach to using a Genetic Algorithm (GA) within a YOLOv5 framework for human object detection applied in the Unmanned Aerial Vehicle (UAV) perspective image dataset. The dataset has challenges, such as a small target, the view of the object is from above, and there is an illumination and light effect. To comply with this challenge, we will utilize the dataset of visual images taken from a UAV (RGB-image) along with Thermal Infrared (TIR) information. GA is used for optimizing the Hyperparameter, which is one of the critical factors in determining the model's performance. Based on our numerical experiments, we found that this YOLOv5-based transfer learning method using RGB-TIR dataset and optimized by GA can achieve higher accuracy compared with the original YOLOv5 for Human Detection on Unmanned Aerial Vehicle Perspective. The objective of this research is to create a surveillance system that will be used to monitor a wide area using autonomous UAVs that can exchange information with each other. In the end, the solution from this research can help related parties in tackling the problem of illegal activities with limited human resources.

Keywords: Signal Processing, Smart Buildings, Smart Structures
Abstract: Deep learning breakthrough stimulates the new research trend in infrastructure defects inspection. The lack of quality-controlled, human-annotated, free of charge, and publicly available defect datasets with sufficient amounts of data hinders the progress of deep learning in defects inspection. To boost research in deep learning-based defects inspection, we first summarize 37 publicly available defect datasets in this two-part survey. These defect datasets cover common defects in various types of infrastructure, and the taxonomy of the datasets is based on specific deep learning objectives. Besides, taking crack as the research target, we have combined the existing datasets with self-labeled crack images to establish a benchmark dataset for crack classification and segmentation. Moreover, based on the established crack dataset, we make a comprehensive comparison between state-of-the-art algorithms for classification, segmentation, and detection. In this paper, we concentrate on datasets and algorithms for defect classification. Altogether 11 classification-oriented defect datasets are summarized and demonstrated in detail. Based on the established crack dataset, we comprehensively compare existing state-of-the-art algorithms for object classification, which provides a baseline for future research in defects inspection. The companion paper of this work surveys datasets and algorithms for defect segmentation and detection, where 26 defect datasets are elaborated, and systematic comparison between state-of-the-art algorithms has been conducted. The classification algorithms illustrated in this paper are conducive to the coarse localization of the defects' position in images.

Keywords: Signal Processing, Smart Buildings, Smart Structures
Abstract: The lack of publicly available defect datasets with qualified annotations has restricted the development of deep learning-based infrastructure defects inspection. This two-part survey aims to boost research in deep learning-based defects inspection. In the companion paper of this work, we have presented a systematic survey on 11 classification-oriented defect datasets and comprehensively compared the state-of-the-art algorithms for object classification, taking crack as our research interest. Besides, we have summarized the datasets before and incorporated our self-labeled crack data to establish a benchmark dataset and proposed suggestions for building a high-quality dataset for defects classification. In this paper, we first present a systematic survey on 26 publicly available datasets for defect segmentation and detection. We then conduct experiments on the established defect dataset for the crack segmentation and the subsequent detection tasks based on non-maximum suppression. The results show that our proposed method, based on our previous work, has achieved satisfactory performance compared with the state of the arts. Our method also shows superior performance with acceptable efficiency on modern graphic processing units. Moreover, we have also performed a detailed illustration of the typical segmentation networks and a comprehensive comparison between existing state-of-the-art algorithms for crack segmentation, which provides a comprehensive baseline for future research in defects inspection.

Keywords: Learning-based Control, Automated Guided Vehicles, Nonlinear Systems and Control
Abstract: This paper presents an online neural-network (NN) learning algorithm applied to a Model Predictive Control (MPC) controller for a propeller-tilting hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). Neural networks are trained online to learn unknown dynamics and disturbances acting on the system to reduce the error between the actual dynamics of the system and the nominal dynamics considered in the MPC algorithm. Online learning is based on two theorems which provide Lyapunov proofs and guarantees of convergence for the tracking error between the desired and the real dynamics. The results presented in this work show that the proposed online-learning algorithm yields significant improvements in the tracking of the desired trajectory, even in the case of a fault in one of the actuators. Simulations prove that this controller is suitable for a complex system such as the tilt-rotor VTOL UAV.

Keywords: Nonlinear Systems and Control, Adaptive Control, Control Applications
Abstract: This study proposes an adaptive super-twisting sliding mode back-stepping controller (ASTSMBC) for hypersonic flight vehicles (HFVs) to hit maneuvering targets considering the impact angle constraint and autopilot dynamics. Firstly, a nonlinear longitudinal model of HFV is established, which formulates the relation between line-of-sight (LOS) angle and trajectory control variables. Then, an integrated guidance and control (IGC) scheme composing of back-stepping logic and sliding mode control technique is designed to guarantee the impact angle converge to the expected angle in finite time. Furthermore, the global stability of the closed-loop system is proved by utilizing the Lyapunov theory. Finally, numerical simulations demonstrate the effectiveness of the proposed scheme.

Keywords: Control Applications, Nonlinear Systems and Control
Abstract: This paper presents a novel approach of designing saturated Higher-Order Sliding Mode (HOSM) controllers for a class of DC/DC converters. Specifically, the proposed controller aims at guaranteeing boundedness and smoothness of the duty cycle feeding the Pulse-Width-Modulator. The novel control architecture consists of the so-called Bounded Integral Control (BIC) combined with a discontinuous HOSM control algorithm. The main strength of the proposed approach is its general applicability to a large class of DC/DC converters. Numerical results testify the effectiveness of the proposed approach.

Keywords: Nonlinear Systems and Control, Fault Detection and Diagnostics, Adaptive Control
Abstract: In this paper, a robust adaptive fault-tolerant controller (RAFTC) is proposed to solve output tracking control of uncertain magnetic levitation, i.e., maglev, system subjected to time-varying actuator faults. At first, the dynamic model of the maglev system is established, then the model is transformed into linear system via feedback linearization. The time-varying actuator faults including loss of effectivness and bias fault are considered in the controller design based on backstepping method. The adaptive law with smooth projection modification is introduced to deal with the effect of parameter uncertainties and disturbances in the system. The stability analysis of the closed-loop system is given. Finally, simulation results verified the effectiveness of the proposed controller

Keywords: Robotics, Adaptive Control, Modeling and Control of Complex Systems
Abstract: In this paper, we propose a self-balancing mobile robot system and further design a control method to improve the resistance to external impact and the adaptability to continuous loads when the robot is at a fixed point. Firstly, the self-balancing robot we put forward in this paper is compared with the classic two-wheeled self-balancing robot to show its structural advantages. Secondly, the mechanical design part and the electrical design part of the robot are introduced, and the system is mathematically modeled and analyzed. Finally, an adaptive double-fuzzy anti-integral saturation PID control is developed and compared with the dual-loop PID control on the proposed robot. Experiments show that when the robot stops at a fixed position, adaptive double-fuzzy anti-integral saturation PID control could effectively improves the impact resistance and adaptability to continuous loads.

Keywords: Adaptive Control, Nonlinear Systems and Control
Abstract: An adaptive control scheme, combined with prediction-error estimation for uncertainty, is proposed in this paper for uncertain nonlinear systems that are subject to input actuator constraints, including the amplitude and rate saturation. Together with the conventional tracking-error estimation, the prediction-error estimation is used to enhance the estimation of uncertainties in the system, and bring additional stabilizing properties for parameter estimation error, which is absent in conventional adaptive control. An auxiliary dynamics driven by the actuator saturation error is constructed to form an augmented tracking error, which is used in the adaptive law to guarantee the stability of the closed-loop system under actuator saturations. Model uncertainties, such as unknown parameters and disturbance, including the unknown control input gain, are considered in the system. Stability analysis and simulation examples on wing rock in delta-wing aircraft are provided in this study.

Keywords: Optimal Control, Linear Systems
Abstract: The paper presents the Robust Control Toolbox for Time Delay Systems with Parametric Uncertainties for the Matlab system. The toolbox comprises the D-K iteration and the algebraic approach implemented for general 3rd order system with parametric uncertainties in numerator and denominator of plant transfer function and uncertain time delay with factorization of simple feedback controller to the parts in two-degree-of-freedom feedback interconnection. The uncertain time delay is treated using multiplicative uncertainty, the parametric uncertainty is modelled using general interconnection for the systems with parametric uncertainty in numerator and denominator. The toolbox has user-friendly interface empowering intuitive control.

Keywords: Nonlinear Systems and Control, Multi-agent Systems, Adaptive Control
Abstract: This paper investigates the problem of collisionsfree consensus tracking of multi-agent systems. Differing from the existing results where the collision avoidance methods are developed based on reliable communications, the communications between agents are considered to be intermittently interrupted (e.g., due to failures, adversarial attacks, or other agents’ blockages), which will destroy the interagent coordination and thus cause potential collision threats. To address it, a switching barrier-Lyapunov redesign method is proposed and an adaptive barrier function-based feedback gain is introduced into the controller to enhance the coordination and prevent collisions. Under mild assumptions, it is shown that the constructed controller can simultaneously guarantee the tracking performance and collision avoidance between agents. Simulations are given to illustrate the effectiveness of the proposed method.

Keywords: Control of Distributed Generation Systems, Control Applications, Networked Control
Abstract: As a cyber-physical system, alternative current (AC) microgrids (MGs) are vulnerable to cyber attacks which affect communication systems and thus, cause system stability issues. In this paper, a resilient control design algorithm is proposed to solve the secondary voltage restoration problem in AC MGs in the presence of denial-of-service (DoS) attacks. It is theoretically proved that the proposed control method can achieve voltage restoration and guarantee the overall stability of the MG system subject to DoS attacks. In order to illustrate the proposed scheme, simulation studies are carried out based on an islanded AC microgrid system with 4 distributed generators (DGs) built in the MATLAB Simulink environment. The simulation results do show its effectiveness and also verify the established theoretical results.

Keywords: Control of Distributed Generation Systems, Control of Smart Power Delivery Systems
Abstract: This paper proposes a novel predefined-time secondary control framework for an islanded DC microgrid to achieve the current sharing and voltage regulation within a tunable settling time. An integrated error, namely, a positive linear combination of current sharing error and DC bus voltage error is constructed and then used in the design of finite/fixedtime feedback loop. Using the integrated error, instead of the current error and the voltage error separately, a unified finite/fixed-time controller structure is proposed to save the computation resource. In addition, the framework enables us to provide rigorous theoretical analysis of the resulting system, which shows the upper bound of convergence time is dependent on both control and system parameters of DC microgrid and therefore is tunable by adjusting control parameters. Finally, simulation tests are carried out to demonstrate the effectiveness of the methods.

Keywords: Learning-based Control, Control of Smart Power Delivery Systems, Learning Systems
Abstract: False Data Injection (FDI) is a new attack method for power system state estimation, which can effectively bypass system monitoring and defense. Due to the highly complex topology of the grid system, the processing of historical data by machine learning method faces the problem of “curse of dimensionality”. This paper investigates a new data-driven attack detection method in smart grid based on extreme learning machine (ELM) and local linear embedding (LLE). The LLE automatically extracts the deep features of nonlinear structure in high dimensional data, while ELM is adopted as a classifier to classify the extracted features and detect the attack. The performance of the proposed method is demonstrated through simulation implemented on an IEEE 57-Bus power system. The simulation results validate that the proposed method is able to achieve high accuracy in detecting abnormal data, and also has better generalization and real-time ability compared to other existing learning-based FDI attack detection algorithms.

Keywords: Multi-agent Systems, Adaptive Control, Estimation and Identification
Abstract: This paper studies the fault-tolerant consensus problem of a novel class of multi-agent systems with sensor faults and unknown disturbances, in which each agent is represented by two time-scales system. A decentralized observer is proposed to simultaneously estimate the fault and agent’s states. Then, an observer-based distributed protocol is designed to ensure the consensus of the underlying systems. Sufficient conditions for the existence of the proposed observer and consensus protocol are derived in terms of linear matrix inequalities (LMIs). A numerical example demonstrates the effectiveness of the proposed method.

Keywords: Energy Efficiency, Control of Smart Power Delivery Systems
Abstract: Certain photovoltaic (PV) system parameters such as residual losses, are either neglected or assumed as trivial when their dynamics are studied. Even though this may be the case in some approaches, it is proven that residual losses heavily influence the system operation, especially when tackling the problem of locating the maximum power point (MPP) under normal and partial shading irradiance conditions. Particularly, analytic formulas pertaining the mentioned losses are derived in this paper and their effect on MPP tracking is investigated. As losses are greatly affected by the current flowing through the respective element, they can be minimized through optimization techniques. However, both, the issue of minimizing the residual losses and that of tracking the operating condition at MPP, is proposed to be combined into a unique optimization problem. It is important that the combined optimization approach is especially efficient in the case when more than one local maximum exist as happens in partial shading irradiance conditions. The whole analysis and the proposed optimization technique is evaluated under normal and adverse irradiation conditions whereas the simulation results totally verify the validity of the method.

Keywords: Modeling and Control of Complex Systems, Linear Systems, Adaptive Control
Abstract: Under partial loss of braking force of high-speed trains (HSTs), a set of control strategies is proposed to improve the overall braking performance of HSTs by properly distributing available braking forces. Taking full advantage of braking capacity provided by each carriage, optimization control strategies are proposed that, based on accurate reports of available braking forces, allow one to distribute braking force reasonably while maintaining a certain safety margin. The braking force distribution under two different train emergency plans (only loss of electric braking forces) is studied, and four algorithms are demonstrated.

Keywords: Multi-agent Systems, Nonlinear Systems and Control, Robust and H infinity Control
Abstract: In this paper, we investigate the nonconvex L2−L∞ containment control. The objective is to use local information to make all followers move to the convex hull formed by multiple stationary leaders while satisfying a desired L2 − L∞ performance index and the control input of each follower remain in their corresponding constraint set. Based on constraint operator and Lyapunov function, some sufficient condition are given to solve nonconvex L2− L∞ containment control problem.

Keywords: Multi-agent Systems, Nonlinear Systems and Control, Control Applications
Abstract: This paper investigates a tracking control problem of multiple high-speed trains systems under velocity and input constraints, and it also considers the case when couplers be- tween adjacent carriages are flexible. We propose a distributed control protocol to realize the cooperative control of trains with multiple carriages connected by flexible couplers. The analysis approach is based on model transformations, appropriate parameter selection and the convex analysis. Finally, the simulation results further vividly illustrate the effectiveness of the proposed control protocol.

Keywords: Learning-based Control, Learning Systems
Abstract: This paper investigates the false data injection attack (FDIA) locational detection problem for power grid based on edge computing. Since the computing power of the edge devices is limited, the proposed FDIA locational detection method should be computationally lightweight. Meanwhile, to ensure the safe and stable operation of the power grid, the proposed method should have real-time data processing capability. Considering that the early exiting mechanism can accelerate the inference of convolutional neural network (CNN) and the quantization method can reduce the computation burden, these two mechanisms are combined with the CNN to establish the fast locational detection model. By applying the presented model, the edge computing based FDIA locational detection is realized. Experiments conducted on IEEE 14-bus system show that the FDIA can be accurately detected and located by using the presented fast locational detection model.

Keywords: Control Applications, Intelligent and AI Based Control
Abstract: In order to reduce total delay time and energy consumption of trains in unexpected events which cause operation delay of trains, this paper investigates an optimization scheduling scheme for high-speed trains. First, for the train scheduling model, the objective function is designed which considers the total delay time and energy consumption. Then multiple operation constraints of trains are proposed. Afterwards, to solve the scheduling problem with multiple constraints, an improved adaptive particle swarm optimization (APSO) algorithm is designed which can optimize train delay time and energy consumption to reduce the effect of unexpected events. With the proposed APSO method, the premature convergence can be avoided and more optimal solution can be searched. Meantime, the searching speed can also be guaranteed through the adaptive adjustment strategy for acceleration coefficients. Finally, simulation results are given to illustrate the effectiveness of the proposed APSO algorithm for the train scheduling problem.

Keywords: Discrete Event Systems, Intelligent and AI Based Control, Modeling and Control of Complex Systems
Abstract: The operation of high-speed trains is influenced frequently by emergencies such as strong wind, red strip, foreign object intrusion and so on. In the disrupted situation, the train timetable, as well as the rolling stock and the crew schedule will deviate from their original plans. Thus, how to adjust the resource schedules fast and efficiently is the key problem for dispatchers. The existing sequential rescheduling method may lead to low efficiency or even infeasible solutions. In this paper, a collaborative mixed-integer programming model based on the event-activity network is constructed to solve the cooperation rescheduling problem. With the consideration of the cancellation of trains, the connection of rolling stock and the crew, and the utilization of the inventory, a multi-disciplinary cooperation strategy is proposed. Furthermore, the Beijing-Tianjin intercity railway is utilized to test our cooperation rescheduling strategy through computational experiments. Compared with the sequential train rescheduling method, the proposed collaborative train rescheduling method can decrease the number of cancellations and get more feasible results.

Keywords: Optimal Control, Learning Systems, Nonlinear Systems and Control
Abstract: Neural networks have proven to be an efficient and expressive black-box model to learn complex nonlinear relationships from large amounts of data. They are also increasingly popular in system identification for model predictive control (MPC). One of the main caveats of neural networks in this setting is their lack of uncertainty quantification. Unreasonable predictions in regions without training data might lead to poor and potentially dangerous control behavior. Bayesian neural networks try to alleviate this problem but add significant complexity to both training and inference, rendering their application for MPC infeasible. Bayesian last layer (BLL) is a simplified Bayesian neural network, representing a compromise between tractability and expressiveness. Most importantly, training and point estimates are unchanged in comparison to regular neural networks. The BLL covariance computation is strongly related to Gaussian Processes but in contrast to them, BLL does not suffer from the same scaling issues. While BLL cannot be used for probabilistic guarantees in most cases, we propose to define a trust region based on the computed covariance. We demonstrate in an empirical investigation that our economic MPC formulation with BLL trust region constraint leads to well behaved closed-loop trajectories, where the formulation without trust region leads to poor closed-loop performance.

Keywords: Control Applications, Optimal Control
Abstract: Small power system networks, integrated with renewable energy resources, often pose several technical challenges to maintain the system frequency due to insufficient inertia in the network. To overcome these challenges, the present study proposes a Lyapunov based controller to emulate the inertia which would effectively support the frequency under generation-demand imbalance conditions in a mixed generation system. The parameters of the controller are optimally selected using the Comprehensive Learning Particle Swarm Optimisation (CLPSO) algorithm and the stability conditions of the controller are established and proved using Lyapunov stability criteria. The proposed controller is implemented in MATLAB/SIMULINK considering a 4-bus mixed generation power system network consisting of a single synchronous, diesel and DFIG-based wind generator. The results of the simulation convincingly demonstrate that the proposed inertia emulation controller could support the frequency of the mixed power system under generation-demand imbalance conditions.

Keywords: Control of Distributed Generation Systems, Optimal Control, Energy Efficiency
Abstract: This paper presents the maximization of lead-acid battery lifetime used as a backup in renewable energy (RE) systems, depending on the number of photovoltaic panels (PV) connected to the system. Generally, the most comprehensive lead-acid battery lifetime model is the weighted Ah-throughput (Schiffer) model, which distinguishes three key factors influencing the lifetime of battery: number of bad recharges, time since last full recharge and lowest State of Charge (SOC) since last full recharge. The predicted power required from the battery can be inferred from power production and consumption forecasts. When the RE system consists of batteries, solar panels (PV) and loads, the solar panels connected to the RE can be triggered to on or off mode in order to meet a global efficiency objective. Hence, this paper addresses the PV operating mode management that extends the battery life. A dynamic model of the battery allows to compute the current and SOC. In turn, the SOC is used to determine the three batteries lifetime factors and calculate the degradation capacity loss and thus the Remaining Useful Life (RUL) of the battery. Finally, these models are embedded into an optimization problem which yields the number of PV that should be connected at each time to minimize degradation capacity loss.

Keywords: Learning-based Control, Control Applications, Optimal Control
Abstract: Lithium-ion batteries are complex systems that require suitable management strategies to work properly, achieve fast charging, mitigate ageing mechanisms and guarantee safety. Among the different model-based charging strategies, the use of predictive control has shown promising results, due to its ability to deal with nonlinear systems subject to safety constraints. However, although many implementations have been proposed in the literature, little attention has been paid to their practical feasibility, which is limited by the high computational cost required online. In this paper, we exploit, for the first time in the batteries field, an approximation of predictive control obtained through the use of a deep neural network. The proposed solution is suitable for real-time battery charging, due to the fact that most of the computational burden is addressed offline. The results highlight the effectiveness of the presented methodology in approximating a standard model predictive control solution.

Keywords: Control Applications, Nonlinear Systems and Control, Optimal Control
Abstract: We consider the problem of optimal investment and consumption in a market with Markovian switching coefficients. We further assume that the borrowing interest rate is higher than the lending interest rate. The power utility from consumption and terminal wealth is used as an optimality criterion. Due to different borrowing and lending interest rates, the resulting optimal stochastic control problem has a {it nonlinear} system dynamics with Markovian switching. We obtain an explicit closed-form solution to this problem as a linear state-feedback control the gain of which is determined by a system of coupled {it Bernoulli} backward ordinary differential equations.

Keywords: Robotics, Automated Guided Vehicles, Sensor/Data Fusion
Abstract: This paper presents the results of a preliminary evaluation of the usage of the Light Detection and Ranging (LiDAR) technology for counter-UAS (C-UAS) applications. This evaluation is based on field experimentation involving low-altitude maneuvers by multiple different size and shape small UAS captured with a low-end Velodyne Hi-Res sensor. The paper describes the current state-of-the-art of the 3D 360 LiDAR and algorithm developed to process point cloud data with a goal of decreasing a possibility of a false sUAS detection when operating in a rural environment. The paper verifies limitations of small UAS detection using LiDARs and the effects the low-end sensor has on the detection rate. The paper ends with conclusions and recommendations for future research in the small UAS detection using LiDAR technology.

Keywords: Multi-agent Systems, Networked Control
Abstract: We study mean square output consensus control of heterogeneous multi-agent systems with multiplicative measurement noises. Each agent has a continuous-time linear heterogeneous dynamics with incomplete measurable state, and there are multiplicative noises along with information exchange among agents. We propose distributed cooperative control laws based on distributed observers and the certainty equivalence principle. By output regulation theory and stochastic analysis, we show that if the dynamics of agents are stabilizable and detectable and the communication graph is connected, then the proposed cooperative control law can ensure mean square output consensus. Finally, the effectiveness of our control laws is demonstrated by a numerical simulation for frequency synchronization of a micro-grid.

Keywords: Robotics, Multi-agent Systems, Real-time Systems
Abstract: In this paper, we propose a coverage control system for a multi-robot team with heterogeneous capabilities to patrol or monitor a bounded environment. The capability could be defined as any criterion of robots like remaining power or mobile speed, depending on the purpose. The proposed control system aims to allocate different portions of the environment to the robots according to their capabilities, i.e., the robot with higher capability takes a larger portion of the environment while the robot with lower capability takes a smaller one. We use the block coordinate descent (BCD) method to optimize the location of portions and the partitioning method alternately. A centralized machine is used to synchronize the robots and the gradient of each robot can be computed in a distributed manner. Simulation results are provided to illustrate the performance of the proposed control system.

Keywords: Nonlinear Systems and Control, Optimal Control
Abstract: In this paper, we propose a robust control algorithm for the quadrotor trajectory tracking under operating constraints and disturbances. The control strategy consists of two serial connected controllers by integrating model predictive control (MPC) with active disturbance rejection control (ADRC). We first design a kinematic controller based on MPC and exploit constraints tightening method to guarantee robust constraints satisfaction. The optimal velocity obtained by the MPC optimization problem is set to be the desired velocity of the dynamic controller. To track the desired velocity, a dynamic controller is designed by utilizing an extended state observer (ESO) to actively reject the disturbances caused by external noises and model uncertainties. The whole system is proved to be stable and feasible. Finally, an illustrative example is provided to verify the efficiency and robustness of the proposed robust tracking control strategy.

Keywords: Networked Control, Modeling and Control of Complex Systems, Process Control & Instrumentation
Abstract: To effectively control large-scale distributed sys- tems online, model predictive control (MPC) has to swiftly solve the underlying high-dimensional optimization. There are multiple techniques applied to accelerate the solving process in the literature, mainly attributed to software-based algorithmic advancements and hardware-assisted computation enhancements. However, those methods focus on arithmetic accelerations and overlook the benefits of the underlying system’s structure. In particular, the existing decoupled software-hardware algorithm design that naively parallelizes the arithmetic operations by the hardware does not tackle the hardware overheads such as CPU-GPU and thread-to-thread communications in a principled manner. Also, the advantages of parallelizable subproblem decomposition in distributed MPC are not well recognized and exploited. As a result, we have not reached the full potential of hardware acceleration for MPC. In this paper, we explore those opportunities by leveraging GPU to parallelize the distributed and localized MPC (DLMPC) algorithm. We exploit the locality constraints embedded in the DLMPC formulation to reduce the hardware-intrinsic communication overheads. Our parallel implementation achieves up to 50× faster runtime than its CPU counterparts under various parameters. Furthermore, we find that the locality-aware GPU parallelization could halve the optimization runtime comparing to the naive acceleration. Overall, our results demonstrate the performance gains brought by software-hardware co-design with the information exchange structure in mind.

Keywords: Adaptive Control, Real-time Systems, Optimal Control
Abstract: The goal of this paper is to investigate modelfree data-driven control design strategies for unknown systems. In particular, we report new data-driven linear matrix inequalities (LMIs) and dynamic programming (DP) methods. Both continuous-time and discrete-time systems are considered. We consider data transition equations that include complete information on the system model using state-input trajectories. Instead of computing explicit system model, the data transition equations are used to construct data-dependent LMI and DP formulations. The proposed formulations provide additional insights in data-driven control designs. In addition, we regard the proposed methods as a complement rather than replacement of existing methods.

Keywords: Learning-based Control, Linear Systems
Abstract: This paper develops a method to construct barrier certificates for linear optimal control systems with unknown dynamics. First, a feasible optimization problem is proposed using the relaxed algebraic Riccati equation (ARE) and safety constraints. The proposed optimization problem is developed to find the maximum barrier-certified region while minimizing a predefined cost function over a safety-certified region. The proposed approach can cope with the possible conflicts between safety and stability without using a relaxation factor to simultaneously satisfy both stability and safety. Since this optimization problem is computationally hard to solve, a safe policy iteration method is implemented, and in each iteration, the problem is split into several smaller sum of squares (SOS) programs. Furthermore, an online data-driven approach is proposed to remedy the requirement of complete knowledge about the system dynamics by employing a safe off-policy reinforcement learning algorithm to solve the proposed optimization problem. Applying the off-policy method allows learning about safe optimal policy while using a different safe exploratory policy to collect data. In the end, to demonstrate the efficacy of this method, a numerical example is provided.

Keywords: Learning Systems, Modeling and Control of Complex Systems, Flexible Manufacturing Systems
Abstract: The problem of pod retrieval in Robotic Mobile Fulfilment System (RMFS) is a key problem to improve the order picking efficiency. In such system, each robot needs to complete a set of retrieval requests, including bringing each pod from a retrieval location to a picking station and return the pod to a storage location. The objective is to minimize the total cost for each robot with all retrieval requests completed. In the previous literature, the problem was viewed as a static combinatorial optimization problem, which was commonly solved by heuristic methods. This kind of approachs often face with computational efficiency problems and are hard to satisfy the real-time requirement in complex real scenes. In this paper, we formulate the problem as a Markov Decision Process, a kind of Sequential Decision Making Problem, and then using Transformer with reinforcement learning to learn an efficient retrieval policy. The effectiveness of the method is verified by experiments.

Keywords: Learning-based Control, Nonlinear Systems and Control
Abstract: Control realization for power electronics systems via deep reinforcement learning (DRL) has evidently shown its superiority over conventional model-based control design methods in recent years, resulting from its adaptive and self-optimization capabilities. However, the inevitable gap between offline-trained agent and the real-life system becomes a key issue hindering its practical implementations. In order to enhance the robustness of DRL controller for DC-DC buck converter systems feeding constant power loads (CPLs), this paper proposes a novel composite DRL controller by fusing an extended state observer (ESO). Such handling approach is able to compensate the mismatched lumped terms between offline-trained agent and real-life platform, hence effectively improving the steady-state performance in practical implementations, while an optimized transient-time control performance of the system is achieved. The feasibility of the method is verified by Matlab/simulation platform between PI controller and the proposed algorithm.

Keywords: Learning Systems, Control Applications, Learning-based Control
Abstract: A hierarchical learning framework (HLF) is validated on a rheostatic brake emulator called Electrical Braking System (EBS). The three-level learning starts with indirect closed-loop feedback linearization at level L1 by using input-output data samples collected from the EBS under exploration settings. A virtual state representation serves for feedback control in order to reach a linear model reference tracking, the solution being learned with a value iteration reinforcement learning approach. On top of the linearized closed-loop control system (CLCS), a secondary level L2 learning phase takes place, with an experiment-driven Iterative Learning Control (EDILC) used for learning reference input-controlled output pairs called primitives. The intent is to make the CLCS’s output have a shape with adequate approximation capacity. Learning is done by repetitions here, however, the final learning level L3 uses the level L2 learned primitives to predict the reference input ensuring optimal tracking of an unseen before desired trajectory, this time without repetitions. The proposed HLF displays features that are specific to intelligent organisms: memorization, learning and generalization of previously learned behavior.

Keywords: Optimal Control, Learning Systems, Learning-based Control
Abstract: In this paper, we present a delay compensating Q-learning algorithm for solving the optimal stabilization problem of a magnetically suspended balance beam system. The suspension by electromagnets mimics the operation of active magnetic bearings (AMBs) employed in industrial applications. We focus on the frequently encountered delay arising in the input current channel, which controls the magnetic field to produce the required force. We employ the recently proposed model-free reinforcement learning algorithm based on the idea of state augmentation to bring the balance beam system into a delay-free form. Compared to the recent works, the approach requires neither the knowledge of the system parameters nor the actual value of the delay. Only an upper bound of the delay is needed, which can be selected arbitrarily large as long as the learning time is acceptable. The stabilization of the balance beam and the convergence of the optimal control parameters are shown, confirming the efficacy of the proposed technique.

Keywords: Learning-based Control
Abstract: This paper proposes a method to study unmanned aerial vehicles (UAV) maneuvering decision in air combat based on deterministic policy gradient. Aiming at the problem of decision space continuity, based on reinforcement learning theory, the Deep Deterministic Policy Gradient (DDPG) algorithm architecture is used to overcome the dimensional catastrophe caused by the discretization of decision variables and achieve air combat decisions in a continuous decision space for the problem of continuous decision space. In the design of the reward function, based on the traditional distance and angle evaluation factors, the energy function is added to improve the accuracy of the reward function for air combat situation description. Through autonomous reinforcement learning training, UAV gradually learns to acquire strategies for air combat decision making without the priori knowledge, which enables UAV to gain maneuver dominance advantage in air combat. Simulation experiments show that based on the algorithm model proposed in this paper, UAV can conduct the autonomous learning process, complete the air combat maneuver decision and obtain the dominance advantage according to the air combat maneuver decision-making environment.

Keywords: Multi-agent Systems, Intelligent and AI Based Control
Abstract: Multi-UAV autonomous formation task is one of the important research hotspots in UAV application. A typical mission scenario is constructed for the autonomous formation, maintenance and obstacle avoidance tasks of multi-UAV. Based on Multiple-Agents Deep Deterministic Policy Gradient (MADDPG) algorithm, we designed a hybrid reward distribution mechanism for autonomous formation task, which effectively solved the problem of uneven global reward distribution and individual "selfish strategy", and designed a dynamic communication strategy inside the UAV formation to reduce the computational complexity. It can effectively improve the communication efficiency between UAVs. After training, the UAV can effectively avoid the no-fly zone and efficiently perform autonomous formation flight task. The introduction of the hybrid reward mechanism improves the stability of the UAV autonomous formation task and has certain application prospects.

Keywords: Estimation and Identification, Sensor/Data Fusion
Abstract: Air target recognition is the basis of air combat situational awareness and threat assessment, as well as an important basis for air combat decision-making. Higher and higher target recognition capabilities are required in modern air combat. According to the characteristics of aerial targets, the target features of the entity model are selected. Then, an optimized Multi-entity Bayesian network target recognition model is established, and the inference algorithm of the multi-entity Bayesian network is analyzed. Simulation results show the stability and accuracy of multi-entity Bayesian network in aerial target recognition.

Keywords: Automated Guided Vehicles, Optimal Control, Modeling and Control of Complex Systems
Abstract: In this paper, the problems of road congestion and travel efficiency are solved by designing a Stackelberg incentive mechanism. Based on the game theory, the Intelligent Connected Vehicles (ICVs) are treated as the followers and the Dispatching Center (DC) as a leader in the Stackelberg incentive mechanism. As the information of other ICVs may not be completely obtained, the respective Nash equilibrium solutions of the optimal route under two different conditions are obtained. By designing the Stackelberg incentive mechanism, the profit function of the DC and the utility function of each ICV is maximized simultaneously when all ICVs choose the optimal routes. Finally, using numerical simulations, the effectiveness of the incentive mechanism for two different conditions is proved.

Keywords: Estimation and Identification, Sensor/Data Fusion, Linear Systems
Abstract: Abstract Real-time tracking maneuvering targets is vital for monitor the target. This paper proposes an adaptive turn rate target tracking algorithm based on the interactive multiple model, which estimates the angular velocity online according to the turning radius and velocity of target. An AIMM algorithm is also proposed, which modifies the probability transition matrix online based on the slope of the model prior probability change. The tracking error is reduced and the robustness of the tracking is improved by using this algorithm. Simulation is presented to testify the tracking accuracy and stability of the algorithm proposed in this paper.

Keywords: Modeling and Control of Complex Systems, Motion Control, Control Applications
Abstract: In this paper, an anti-swing controller for a double pendulum payload system is proposed using the method of backstepping based on dynamic models. Lagrange equations are used to derive the dynamic equations of the overhead crane payload system in a 2-dimensional plane, which provides an elegant clean form for developing the controller. The anti-swing control system, as well as the simulations, is implemented in real-time via the software tool MATLAB/Simulink. The results prove that the proposed controller can effectively help the system eliminate the induced swing of the payload system in a short period.

Keywords: Sensor/Data Fusion, Man-machine Interactions
Abstract: The research on how to provide effective onboard decision support for surface ships, especially in critical operational scenes such as collision avoidance, has been triggered as a goal in recent years. From the authors' perspective, to achieve this goal, it is crucial to comprehensively understand the operational logic and mechanism of the human navigators. In this paper, we use wearable eye tracker glasses to collect visual attention data from the navigators. The scene is established as a collision avoidance task in a strait water channel on a maritime simulator. By using the concept of critical operations, the whole sailing is divided into cruising and maneuvering time windows. The visual attention is analyzed in terms of the transition time/frequency and area of interest. It is the first time to exclusively analyze the navigators' visual attention in collision-avoidance tasks. The paper suggests a way to potentially predict the human-dominant onboard operations, which builds the basis for developing a better decision support system.

Keywords: Control Education, Real-time Systems, Control Applications
Abstract: The field of mechatronics is a multidisciplinary field of engineering, where the combination of physical components and theory from several engineering fields is applied to build complex machines. Mechatronics education is an active learning process through practical laboratory exercises and problem-based learning. This paper presents the design and implementation of a mechatronics home lab to support undergraduate mechatronics teaching. The purpose is to support theoretical teaching in mechatronics with a low-cost, 3D-printable platform where the students can experiment and practice instrumentation and control theory with a practical problem-based approach. Five projects were introduced for experiment implementation of the developed home lab. Throughout these experiments, it is intended to facilitate the understanding of theories and concepts in mechatronics, and enhance the ability to design and implementation of experiments, the collection and analysis of data, and the conducting of simulation in MATLAB.

Keywords: Adaptive Control, Control Applications, Nonlinear Systems and Control
Abstract: The anti-swing control of offshore cranes presents much more challenges. Most existing controllers for offshore cranes are designed based on linearized dynamics and require the accurate values of the plant parameters. In this paper, an adaptive sliding mode control scheme is investigated for a nonlinear underactuated crane system with unmodeled dynamics. The proposed control method can ensure asymptotic stability and does not need linearization of the complicated nonlinear dynamic equations during controller design and stability analysis. To reduce the communication burden in a network, a uniform quantizer is introduced in the input communication channel. A quantized adaptive sliding mode control scheme is further developed for the underactuated cranes to compensate for the effects of input quantization and uncertain parameters. The proposed controller together with the quantizer ensures the asymptotic stability of the closed-loop system in the sense of signal boundedness and zero stabilization error. Numerical simulations are conducted to illustrate the effectiveness of proposed schemes.

Keywords: Energy Efficiency, Estimation and Identification, Real-time Systems
Abstract: This paper aims to propose an efficient machine learning framework for maritime big data and use it to train a random forest model to estimate ships’ propulsion power based on ship operation data. The comprehensive data include dynamic operations, ship characteristics and environment. The details of data processing, model configuration, training and performance benchmarking will be introduced. Both scikit-learn and Spark MLlib were used in the process to find the best configuration of hyperparameters. With this combination, the search and training are much more efficient and can be executed on latest cloud-based solutions. The result shows random forest is a feasible and robust method for ship propulsion power prediction on large datasets. The best performing model achieved a R2 score of 0.9238.

Keywords: Learning Systems, Intelligent and AI Based Control, Modeling and Control of Complex Systems
Abstract: Offshore operations occur in highly critical environments where safety and trust in the systems are highly relevant. These systems are complex, extremely slender, subject to long transient periods. They need to deal with sparse data measurements and very often unexpected situations occur. Higher level of automation has been proven to be beneficial in tackling these aspects and contribute to a safer and more efficient operation. The research presented in this paper aims to advance the use of autonomous decision-making in critical operations by presenting a framework for automated learning of transition functions model by use of physics-based models of the processes. Transition functions model is a bottleneck in adopting in practice algorithmic techniques such as Markov Decision Problem and Reinforcement Learning. The research presented in the paper addresses challenges in modelling transition functions when few learning data sets are available.

Keywords: Estimation and Identification, Learning Systems, Nonlinear Systems and Control
Abstract: In this paper, we introduce the concept of observability of selected state variables for systems that may not be fully observable. For their estimation, we introduce and exemplify a deep filter, which is a neural network specifically designed for the estimation of selected state variables without computing the trajectory of the entire system. The observability definition is quantitative rather than a yes or no answer so that one can compare the level of observability between different sensor locations.

Keywords: Multi-agent Systems, Nonlinear Systems and Control, Optimal Control
Abstract: This paper presents a practical optimized time-varying formation tracking control method for nonlinear multiagent systems. In general, solving the HJB equation for nonlinear system is difficult and even no analytical solution can be guaranteed. For multi-agent systems, the solution is more challenging due to the state coupling problem. In the proposed optimized scheme, the problems are solved using the reinforcement learning (RL) of identifier-critic-actor architecture. Furthermore, the unknown dynamics are approximated by neural networks, which are integrated into the controller. Based on the principle of optimality and Lyapunov theory, the optimality and stability of multi-agent systems are proven, respectively. Finally, the simulation results illustrate the effectiveness of the acquired theories.

Keywords: Learning Systems, Micro and Nano Systems
Abstract: The distributions of velocity and pressure in blood vessels are essential information for the diagnosis and treatment of vascular diseases. Conventional medical imaging techniques such as ultrasound Doppler and computer tomography are suitable for point measurement of blood velocity. The reconstruction of velocity and pressure distributions are challenging and time-consuming. Even digital subtraction angiography provides the spatiotemporal concentration of the contrast medium in blood vessels, the blood velocity is evaluated empirically by physicians in the clinic. It is still challenging to infer the flow field information from the concentration distribution. In this study, we propose a novel inferring method to reconstruct the velocity field and the pressure field from a fractional sampling of the concentration field. This method combines the physics-informed neural network (PINN) algorithm with gradient limitations, referring to as the gradient-limited PINN (GL-PINN). The results demonstrate that the GL-PINN algorithm is capable of inferring the velocity field and pressure field from the concentration field. The calculation error is less than 1% compared with Comsol results. Moreover, the GL-PINN algorithm with gradient constraints shows a better accuracy than the traditional PINN algorithm. The proposed GL-PINN is promising in inferring the blood velocity and pressure from DSA for the diagnosis of vascular diseases.

Keywords: Intelligent and AI Based Control, Learning-based Control, Control Applications
Abstract: In this paper, we present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. In particular, we provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology in order for the latter to execute optimal safe performance which is measured through certain Key Performance Indicators (KPIs). The network consists of a set of fixed Base Stations (BS) which are equipped with antennas, which one can control by adjusting their vertical tilt angles. The aforementioned process is called Remote Electrical Tilt (RET) optimization. Recent research has focused on performing this RET optimization by employing Reinforcement Learning (RL) strategies due to the fact that they have self-learning capabilities to adapt in uncertain environments. The term safety refers to particular constraints bounds of the network KPIs in order to guarantee that when the algorithms are deployed in a live network, the performance is maintained. In our proposed architecture the safety is ensured through model-checking techniques over combined discrete system models (automata) that are abstracted through the learning process. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions, and those that are allowed and blocked according to the safety specification.

Keywords: Fault Detection and Diagnostics, Learning-based Control, Sensor Networks
Abstract: A mechanism to protect a controlled system in the event of a priori unknown abnormalities (e.g. faults, attacks) is the key to designing resilient and robust control systems. We explore bi-level control design architectures in which a supervisory Reinforcement Learning (RL) agent augments an over-observed controlled system. The RL agent monitors sensor signals, detects and takes action to mitigate unknown sensor faults. We use the system dynamics to extract features and develop a design method for the cost function of the RL module. We theoretically show that the designed cost function has a unique optimal policy that enables the diagnosis of arbitrary constant sensor faults. To conceptualize our architecture, we consider a linear version of an over-observed chemical process, controlled by a Linear Quadratic Gaussian (LQG) Servo-Controller with Integral Action. Our experimental results, coupled with our theoretical analysis, show that the RL-agent is successful in identifying and mitigating the faults in one or more sensors in an online fashion.

Keywords: Sensor Networks
Abstract: This paper is mainly concerned with the problem of distributed state estimation of nonlinear systems over a wireless sensor network under restricted bit rate. Taking into account the network transmission burden, an event-based encoding and decoding method is proposed to schedule the transmission measurement of each sensor. Under a synchronism schedule, the distributed filtering is derived in this paper. By constructing the Lyapunov function, sufficient conditions of the existence of distributed filtering are given. Finally, a simulation example is presented to certify the effectiveness of the proposed algorithm.

Keywords: Automated Guided Vehicles, Control Applications, Nonlinear Systems and Control
Abstract: This paper develops a distributed bearing-only method for the splitting and merging control of multiple underactuated autonomous surface vessels (ASVs) under GPS-denied and obstacle-cluttered environment. Most of the distributed results are implementable based on the assumption that the position of each ASV is accessible. However, it is somewhat idealistic since the loss of the communication network and GPS signals possibly occurs due to various interferences, like the same-band signals and intentional cyber attacks. To this end, a bearing-only formation splitting and merging method is presented in this context. With bearing constraint only, the ASV formation is able to maneuver in GPS-denied environment, while the formation splitting and merging strategy renders it possible for multiple ASVs to automatically resize and splitting when facing signal-blocking obstacles. Simulation results have revealed that the formation keeping and obstacle avoidance can be achieved upon using the proposed method, even with partial losses of the visual connections among ASV members.

Keywords: Adaptive Control, Nonlinear Systems and Control
Abstract: In this paper, the distributed formation control problem for unmanned surface vessels (USVs) with sensing range and communication resource constraints is investigated. An edge-event triggered sliding mode controller is developed to achieve formation-keeping maneuver. Firstly, in order to preserve the prescribed communication connectivity, a combination of artificial potential function technique and the sliding mode surface is adopted in controller design process. Subsequently, considering the limited communication resource, the edge-event triggered control method is introduced to design triggering condition related to the information of every edge. Different from the traditional node-based event-triggered control mechanism, each member only transmits its state information to its out-neighbours asynchronously once the edge between them is triggered, such that the communication cost can be further reduced. The theoretical results exhibit that all signals in the closed-loop system are bounded. Eventually, by resorting to numerical simulations, the validity of the proposed controller is demonstrated.

Keywords: Estimation and Identification, Control Applications
Abstract: Accurate estimation of battery state-of-charge (SOC) is important for safe and reliable operations of batteries. To account for time-varying model parameters, recursive least squares and extended Kalman filter (EKF) are usually combined to jointly estimate model parameters and SOC. However, in practice, the level of excitation in the online data varies with the working condition. In case that the online data are not persistently exciting, the recursive parameter estimation with various forgetting strategies suffers from covariance blowup, To address this issue, a novel adaptive directional forgetting strategy is proposed and used in combination with EKF. Different from forgetting all old data as in the conventional forgetting methods, only the direction with sufficient excitation is forgotten. The forgetting factor is also adjusted adaptively according to the prediction error, which enhances the capability to capture the parameters variations. Using experimental data on three different working conditions, the proposed approach demonstrates improved SOC estimation accuracy compared to using the conventional exponential forgetting.

Keywords: Adaptive Control, Control Applications
Abstract: This paper studies the control synthesis problem for energy harvesting MEMS devices with load-based spectral logic specifications. Firstly, a novel formal language has been proposed to define the specifications on frequency domain with respect to load, called load-based spectral logic. The conditions for specification satisfaction have been derived for the energy harvesting MEMS devices, which transform the formal specification constraints into a set of linear matrix inequalities. Then an iterative control synthesis algorithm has been proposed to find a sequence of static output feedback controllers for the MEMS device, in which the control synthesis problem has been transformed into a sequence of semidefinite programming problems. Finally, a numerical example is provided to illustrate the main results of this paper.

Keywords: Multi-agent Systems, Robotics, Learning-based Control
Abstract: A cooperative control method for unmanned aerial vehicle (UAV) swarm cooperative formation reconfiguration based on the reversed pigeon-inspired optimization (PIO) algorithm is proposed. Firstly, the formation controller is designed to realize UAV swarm formation reconfiguration. Secondly, following the basic idea of PIO, for optimizing the slow convergence speed and falling into local optimum, adjust the updating strategy and topological structure of standard PIO. And the reversed PIO is used to optimize the parameters of UAV swarm formation controller. Finally, through the simulation experiment, it is verified that the UAV swarm can form expectedly, keep the formation under the leader UAV’s complex movement condition and reconfigure under the action of the UAV swarm autonomous formation controller proposed in this paper. The reversed PIO proposed in this paper is compared with the standard PIO, particle swarm optimization (PSO) and genetic algorithm (GA) and the results prove the effectiveness and superiority of the method proposed in this article.

Keywords: Learning-based Control, Multi-agent Systems, Robotics
Abstract: In this paper, a bionic heuristic approach for control augmentation system (CAS) design is proposed to attain better handling qualities in longitudinal channel. Since manual parameter tuning for control parameters is regarded as a time-consuming task, the CAS design procedure can be converted to an optimization problem. A novel algorithm for searching optimal controlling parameters is presented on the basis of pigeon-inspired optimization (PIO) algorithm, through introducing three mechanisms, including self-learning strategy, competitive exploration behavior, and Cauchy mutation. In addition, two typical optimization algorithms are implemented on designing the CAS for comparison. The experiments are carried out on the longitudinal CAS design for F-16 aircraft. Series of comparative results demonstrate that the proposed algorithm can guarantee desirable handling quality.

Keywords: Control Applications, Optimal Control
Abstract: For the phenomenon that traditional genetic algorithm have weak local search ability and are prone to premature in solving combinatorial optimization problems, the local search ability of the genetic algorithm is improved by introducing the explosion operator of the firework algorithm . An improved genetic algorithm, boom genetic algorithm, is proposed. Boom genetic algorithm not only has the advantages of traditional genetic algorithm with strong global search ability, but also has the characteristics of firework algorithm with strong local search ability, which achieves complementary advantages. By using the Traveling Salesman Problem simulation example, the superiority and validity of the improved genetic algorithm proposed in this paper compared with the traditional one are verified.

Keywords: Signal Processing, Adaptive Control, Nonlinear Systems and Control
Abstract: Kernel correlation filtering algorithm is based on the concept of correlation filtering. By introducing methods such as kernel functions and cyclic matrices, the target tracking speed is improved to a new level while ensuring high tracking accuracy. However, its tracking effect is not good when the target changes at multiple scales and the target is blocked. In view of the above problems, this paper proposes a multi-scale calculation method based on depth information optimization to solve the problem of multi-scale changes in the target. Based on the introduction of occlusion detection, a kernel-correlation filtering algorithm based on Kalman filtering is proposed to solve the problems of occlusion and jitter problem. The improved algorithm is tested on the TOB-50 data set and compared with the original algorithm. The results show that the improved algorithm proposed in this paper has better performance than the original algorithm when the target scale changes, occlusion, and screen jitter.

Keywords: Modeling and Control of Complex Systems, Nonlinear Systems and Control, Control Applications
Abstract: This paper presents a composite control scheme based on linear active disturbance rejection control (LADRC) and dead zone compensation technique to solve the problem of dead zone behavior and reduce sensitivity to disturbance in industrial bridge crane control systems. The poor control performance caused by dead zone problem in bridge crane system is addressed by constructing a smooth inverse function to approach its inverse so as to remove its bad influences. An extended state observer is developed to estimate the “lumped disturbance” caused by parameters perturbations, external disturbances and unmodeled dynamics in real time, and a linear control law is proposed to attenuate the disturbance and achieve angle and position control based on LADRC. Both simulation and experiment results show that the proposed method not only can compensate the adverse influences caused by dead zone, but also have better steady-state accuracy and anti-disturbance performance.

Keywords: Control Applications, Fuzzy and Neural Systems, Nonlinear Systems and Control
Abstract: This paper investigates the control design for nonlinear connected vehicles (CV) systems with unknown actuator faults and saturation. A nonlinear model of the CV’s longitudinal movement is established, in which the unknown nonlinearity (e.g., the nonlinear dynamics, time-varying faults and saturation) is approximated via the adaptive fuzzy mechanism, and a nonlinear disturbance observer is established to estimate the lumped external disturbance. Based on the new model, a fuzzy adaptive sliding mode (FASM) controller with PID sliding mode (PIDSM) surface is designed, which can simultaneously achieve the objective of both individual vehicle stability and string stability under zero initial spacing error. Finally, in order to verify the effectiveness and superiority of the proposed method, experiments were carried out on four Ackermann cars.