Article
Computer Science, Artificial Intelligence
Hao Yu, Tongwen Chen
Summary: This article proposes an adaptive tracking controller based on radial basis function neural networks (RBFNNs) for nonlinear plants with unmatched uncertainties and smooth reference signals. Valid RBFNN adaptive control is introduced, ensuring that all closed-loop arguments of the involved RBFNNs remain inside their corresponding compact sets. A novel iterative design method is proposed and embedded into the traditional backstepping approach to obtain valid RBFNN adaptive controllers.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yongchao Liu, Qidan Zhu
Summary: In this article, the issue of adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems is studied using the backstepping algorithm. Compared with previous research, the difficulty of unknown virtual control coefficients in control design is overcome. The recursive construction of the asymptotic tracking controller is achieved through the use of bound estimation scheme, smooth functions, and approximation-based neural network, ensuring asymptotic convergence character and stability with stochastic disturbance and unknown UVCC with the help of Lyapunov function and beneficial inequalities. This theoretical finding is verified through a simulation example.
Article
Computer Science, Artificial Intelligence
Ji-Dong Liu, Ben Niu, Yong-Gui Kao, Ping Zhao, Dong Yang
Summary: This article proposes a decentralized adaptive finite-time tracking control scheme for a class of nonstrict feedback large-scale nonlinear interconnected systems with disturbances. By introducing command filter technique and adaptive neural control, it overcomes the obstacles caused by unknown interconnections and demonstrates the convergence of closed-loop signals in almost fast finite time.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Yingying Fu, Jing Li, Shuiyan Wu, Xiaobo Li
Summary: This paper studies the dynamic event-triggered tracking control issue for unknown stochastic nonlinear systems with strict-feedback form. Neural networks are used to approximate unknown nonlinear functions and a dynamic event-triggered controller is designed through adaptive backstepping method, with dynamically adjusted triggered threshold. Compared to static event-triggered mechanism, the dynamic event-triggered mechanism can generate larger execution interval and save resources, verified by simulation examples showing closed-loop stochastic system signals are ultimately bounded.
Article
Computer Science, Artificial Intelligence
Haibin Sun, Linlin Hou, Yunliang Wei
Summary: This paper presents a decentralized dynamic event-triggered output feedback adaptive fixed-time funnel controller for interconnected nonlinear systems. By designing a novel dynamic event-triggered mechanism and using a decentralized linear filter, along with the addition of a power integrator technique and a neural network method, the controller achieves improved tracking performance and effectively reduces the computational burden, while ensuring that the tracking error falls within a preset performance range.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Hongyao Li, Fuli Wang
Summary: This paper investigates the problem of adaptive neural network reinforcement learning tracking control for continuous time switched stochastic nonlinear systems with unknown control coefficients and full-state constraints. A set of reconstructed states is defined and switched state observers are developed to handle the unknown control coefficients. An adaptive RL controller is developed to improve tracking performance using the minimal learning parameter method and RL control design technique. The boundedness of the tracking error and all signals is demonstrated through the average dwell time method and tangent type time-varying barrier multiple Lyapunov functions. The effectiveness of the proposed scheme is verified through two examples.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yongliang Zhan, Yongming Li, Shaocheng Tong
Summary: The aim of this article is to study a fuzzy-based decentralized adaptive control strategy for the nonstrict-feedback fractional-order nonlinear large-scale systems with unknown control directions. Fuzzy logic systems are employed to identify unknown nonlinear functions. A Nussbaum function technique is adopted to handle the difficulties caused by unknown control directions. Furthermore, a fuzzy-based decentralized adaptive control strategy is formulated by introducing the dynamic surface control technique into the adaptive backstepping recursive design algorithm. The stability of the controlled system and the convergence of the tracking errors are proved by constructing the fractional-order Lyapunov functions. The validity and effectiveness of the designed decentralized control scheme are confirmed via two simulation examples.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Kaixin Lu, Zhi Liu, Haoyong Yu, C. L. Philip Chen, Yun Zhang
Summary: This article proposes a decentralized adaptive neural inverse approach to address the complexity and time-consuming nature of solving Hamilton-Jacobi-Bellman (HJB) equations in decentralized optimal control of continuous-time nonlinear interconnected systems. By introducing a new criterion of inverse optimal practical stabilization and utilizing adaptive neural strategies, a decentralized inverse optimal controller is designed, demonstrating bounded closed-loop signals and achieving the goal of inverse optimality.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Mathematics
Haifeng Huang, Mohammadamin Shirkhani, Jafar Tavoosi, Omar Mahmoud
Summary: This paper presents a new method for comprehensive stabilization and control system design for stochastic nonlinear systems using a type-3 fuzzy neural network to estimate parameters. Simulation results show that the proposed method has a good performance and can be applied to systems in this class.
Article
Automation & Control Systems
Lijun Long, Fenglan Wang
Summary: This article presents a dynamic event-triggered adaptive neural network control approach for switched nonlinear systems. By utilizing switched command filter and common Lyapunov function method, the issues of asynchronous switching and discontinuous measurement error are addressed. Numerical examples demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Jiapeng Liu, Qing-Guo Wang, Jinpeng Yu
Summary: This paper presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation. The scheme addresses uncertainties in subsystems by using command filters to reconstruct virtual control functions, and employs a piecewise continuous function to deal with the unknown input saturation problem. An event-triggered tracking controller is developed using adaptive neural network technique. Simulation studies validate the effectiveness of the controller.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Xiong Yang, Mengmeng Xu, Qinglai Wei
Summary: This article proposes a simultaneous policy iteration (SPI) algorithm to solve the H-infinity control problem of nonlinear systems with unavailable dynamics and asymmetric saturating actuators. The SPI algorithm converts the control problem into a zero-sum game and solves the corresponding Hamilton-Jacobi-Isaacs equation. A critic, an actor, and a perturbation neural network (NN) are constructed to estimate the cost function, control policy, and perturbation, respectively. The SPI algorithm allows arbitrary control policies and perturbations in the learning process and does not require the persistence of the excitation condition.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yongchao Liu, Qidan Zhu
Summary: In this article, an event-triggered adaptive neural network (ANN) control strategy is developed for stochastic nonlinear systems with state constraints and time-varying delays. The state constraints are handled using the barrier Lyapunov function, and neural networks are utilized to identify unknown dynamics. The use of the Lyapunov-Krasovskii functional helps counteract the adverse effects of time-varying delays. The controller design incorporates the backstepping technique and event-triggered mechanism (ETM) to minimize data transmission and save communication resources.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kewen Li, Yongming Li
Summary: This article introduces a finite-time neural network adaptive dynamic surface control design for single-input single-output nonlinear systems. The control algorithm utilizes a novel nonlinear filter and finite-time Lyapunov stable theory to ensure tracking error convergence within a small neighborhood of origin in finite time. Simulation examples demonstrate the superiority and effectiveness of the proposed control algorithm.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hao Yu, Tongwen Chen
Summary: This article investigates the application of neural network adaptive control in strict-feedback nonlinear systems with matched uncertainties and event-triggered communication. It proposes the concept of valid compact sets to ensure the effectiveness of control. It also introduces an event-triggering mechanism to avoid Zeno phenomenon and save communication resources. Simulation results demonstrate the effectiveness and feasibility of the proposed methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Kamal Mammadov, Cheng-Chew Lim, Peng Shi
Summary: In this manuscript, we formulate the general Target-Attacker-Defender differential game in both continuous-time and discrete-time turn-based variants in n-dimensional Euclidean space. The objective of the Attackers is to get as close as possible to the Target before collision with the Defender, while the Target and Defender coordinate to achieve the opposite. We consider the most general setting for this zero-sum differential game, where the agents can move at different speeds, and prove the Nash equilibrium strategies in the discrete-time turn-based variant.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Ting Shi, Peng Shi, Liping Zhang
Summary: This paper investigates the leader-following consensus problem for general linear multi-agent systems under external disturbances. The communication topologies are time-varying and switched from a finite set. A switched control system is introduced to model these topologies, and the weighted L-2 - L-infinity performance is analyzed. A topology-dependent controller is designed based on local information from the neighbors. Conditions are developed for the existence of a control protocol that achieves the leader-following consensus with a certain level of weighted L-2 - L-infinity performance. The design algorithm is formulated as a set of linear matrix inequalities (LMIs), and a numerical example is provided to demonstrate the effectiveness of the proposed consensus algorithm.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Renjie Ma, Peng Shi
Summary: This paper presents defense strategies based on switched counteraction principle to protect the secure state estimation (SSE) of Cyber-Physical Systems (CPSs) from sparse data injection (DI) attacks. The physical layer is modeled using a hybrid mechanism and malicious injections are excluded through adaptively switched counteraction searching. The proposed design methods are demonstrated to be effective and promising through numerical examples.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Ning Zhao, Peng Shi, Wen Xing
Summary: This article introduces a resilient event-triggered communication mechanism to address the issues of network congestion and redundant communication caused by DoS attacks in networked control systems. By adjusting the threshold parameter in the event-triggered condition, designing an event-driven control protocol, and constructing a new switched NCS model, the exponential stability and Script capital L-2-gain performance of the system are ensured. Additionally, a co-design scheme for the parameters in the event-triggered condition and controller gain is provided for improved system performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Hongjun Yu, Lihua Liang, Peng Shi, Qing Jiang
Summary: This paper introduces a novel approach to calculate roadmaps for robots in unknown environments, utilizing rangefinder readings to establish sequential polygons and iteratively compute intersections. The method results in stable polygon shapes, from which a roadmap is constructed along with a routing algorithm for path calculation. Simulation examples are provided to demonstrate the effectiveness of the proposed approach.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2021)
Article
Automation & Control Systems
Mifeng Ren, Junghui Chen, Peng Shi, Gaowei Yan, Lan Cheng
Summary: This paper presents a two-layer MPC hierarchical architecture for non-Gaussian stochastic processes within the framework of statistical information, achieving control through dynamic economic optimization and reference tracking. The upper layer estimates optimal trajectories used as reference trajectories for the lower layer control system, while a survival information potential-based MPC algorithm is used to maintain controlled variables at their reference trajectories in nonlinear systems. The stability of closed-loop system dynamics is proven using statistical linearization, with examples demonstrating the benefits of the proposed economic optimization and control method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Yulong Huang, Yonggang Zhang, Peng Shi, Jonathon Chambers
Summary: This article introduces a new variational adaptive Kalman filter using a Gaussian-inverse-Wishart mixture distribution for linear systems with partially unknown state and measurement noise covariance matrices. By establishing a hierarchical Gaussian model, the system state vector and noise covariance matrices are jointly estimated. Examples are provided to demonstrate the effectiveness and potential of this new filtering design technique.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Peng Shi, Jiafeng Yu
Summary: In this article, a novel polynomial fuzzy modeling approach is proposed to address the dissipativity-based consensus problem for polynomial fuzzy multiagent systems with switching directed topologies. A consensus control protocol is designed to ensure that the MASs under switching topologies can reach agreement, with conditions presented for exponential consensus with a strictly dissipative performance. The effectiveness of the new design scheme is demonstrated through an illustrative example.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Yang Fei, Peng Shi, Cheng-Chew Lim
Summary: This paper studies the formation control problem for second-order multi-agent systems with practical issues like mismatched uncertainties and obstacle avoidance. A reference correction algorithm based sliding mode control scheme is proposed to tackle the challenges, and both disturbance observer and artificial potential field are implemented to address obstacle avoidance with the existence of mismatched uncertainties. The effectiveness of obstacle avoidance and boundedness of position tracking error are verified by Lyapunov stability theory in the end.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Computer Science, Artificial Intelligence
Min Li, Peng Shi, Ming Liu, Yingchun Zhang, Shuoyu Wang
Summary: This article introduces a new event-triggered adaptive sliding mode control scheme to address actuator failures and signal quantization in a class of T-S fuzzy systems, ensuring the reachability of the proposed sliding surface with the designed control scheme. Additionally, the existence of minimal inter-event time and sufficient conditions to avoid Zeno behavior are analyzed and presented.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Bo Ding, Dezhi Xu, Bin Jiang, Peng Shi, Weilin Yang
Summary: This article proposes a terminal sliding mode control strategy for the speed tracking problem of permanent-magnet linear synchronous traction systems in urban rail transit, utilizing a nonlinear disturbance observer (NDO) and prescribed performance method. The system design combines prescribed performance control, terminal sliding mode control with backstepping, and disturbance estimation using NDO for feedforward compensation, demonstrating effectiveness and advantages through computer simulations and experiments in dSPACE.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Engineering, Marine
Yuanjie Ren, Lanyong Zhang, Peng Shi, Ziqi Zhang
Summary: A hierarchical collaborative control energy management scheme is proposed for the propulsion system of hybrid electric ships. The scheme effectively solves the problems of steady-state oscillation and deviation from the tracking direction caused by volatility and uncertainty, achieving significant improvement.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Huiyan Zhang, Hao Sun, Peng Shi, Luis Ismael Minchala
Summary: This article proposes a novel chip detection method that combines attentional feature fusion and cosine nonlocal attention to effectively handle chip images with multiple classes or complex backgrounds. Experimental results demonstrate that the proposed method outperforms the benchmark method on a medium-scale dataset.
Article
Remote Sensing
Yang Fei, Yuan Sun, Peng Shi
Summary: In this study, a hierarchical formation control strategy is used to address the robust formation control problem for a group of UAVs with system uncertainty. A sliding mode neural-based observer is constructed to estimate the nonlinear uncertainty in the UAV model, and sliding mode controllers and differentiators are designed to alleviate chattering in the control input. The proposed control scheme's effectiveness is validated through Lyapunov stability theory and numerical simulations on a multiple-UAV system.
Article
Automation & Control Systems
Yong Xu, Mei Fang, Peng Shi, Ya-Jun Pan, Choon Ki Ahn
Summary: This paper investigates the event-triggered containment control problem of multiagent systems with a directed graph, proposing a novel protocol for scheduling communications between agents and enabling agent-to-agent data transmission through specific local events. The study develops sufficient conditions for solving the CC problem under this protocol and extends it to an adaptive event-triggered protocol without the need for global knowledge, while also deriving positive lower bounds on interevent time intervals to eliminate the Zeno phenomenon.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
