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Event-triggered receding horizon control via actor-critic design

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Science China(Information Sciences) 2020年第5期63卷 131-145页

作者： Lu DONG Xin YUAN changyin sun School of Automation Southeast University College of Electronics and Information Engineering Tongji University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

In this paper, we propose a novel event-triggered near-optimal control for nonlinear continuoustime systems. The receding horizon principle is utilized to improve the system robustness and obtain better dynamic control performance. In the proposed structure, we first decompose the infinite horizon optimal control into a series of finite horizon optimal problems. Then a learning strategy is adopted, in which an actor network is employed to approximate the cost function and an critic network is used to learn the optimal control law in each finite horizon. Furthermore, in order to reduce the computational cost and transmission cost, an event-triggered strategy is applied. We design an adaptive trigger condition, so that the signal transmissions and controller updates are conducted in an aperiodic way. Detailed stability analysis shows that the nonlinear system with the developed event-triggered optimal control policy is asymptotically *** results on a single-link robot arm with different noise types have demonstrated the effectiveness of the proposed method.

关键词： event-triggered control receding horizon control actor-critic design optimal control nonlinear systems neural networks

Self-triggered Consensus Control for Linear Multi-agent Systems With Input Saturation

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 150-157页

作者： Yanxu Su Qingling Wang changyin sun School of Automation Southeast UniversityNanjing 210096and also with the Key Laboratory of Measurement and Control of Complex System of EngineeringMinistry of EducationSoutheast UniversityNanjing 210096China

In this paper, we study the consensus problem for a class of linear multi-agent systems(MASs) with consideration of input saturation under the self-triggered mechanism. In the context of discrete-time systems, a self-triggered strategy is developed to determine the time interval between the adjacent triggers. The triggering condition is designed by using the current sampled consensus error. Furthermore, the consensus control protocol is designed by means of a state feedback approach. It is shown that the considered multi-agent systems can reach consensus with the presented algorithm. Some sufficient conditions are proposed in the form of linear matrix inequalities(LMIs) to show the positively invariant property of the domain of attraction(DOA). Moreover, some sufficient conditions of controller synthesis are provided to enlarge the volume of the DOA and obtain the control gain matrix. A numerical example is simulated to demonstrate the effectiveness of the theoretical analysis results.

关键词： Input saturation linear matrix inequalities(LMIs) multi-agent systems(MASs) self-triggered control

Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 227-238页

作者： Xiaofeng Li Lu Dong changyin sun the School of Automation Southeast UniversityNanjing 210096 the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China the College of Electronics and Information Engineering Tongji UniversityShanghai 201804China

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching *** system state is forced to track the reference signal by minimizing the performance ***,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled *** linear-in-parameter(LIP)neural network is taken as the value function *** the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable ***,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is ***,the effectiveness of the algorithm is demonstrated with three simulation examples.

关键词： Adaptive dynamic programming approximation error data-based control Q-learning switching system

Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance

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Frontiers of Information Technology & Electronic Engineering 2021年第7期22卷 986-1001页

作者： Xuerao WANG Qingling WANG changyin sun School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

In this paper,an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation.A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external *** guarantee the prescribed performance,an error transformation is applied to transfer the time-varying constraints into a constant ***,by employing a barrier Lyapunov function and the backstepping technique,an observer-based tracking control strategy is *** is proven that using the proposed algorithm,all the closedloop signals are bounded,and the tracking errors satisfy the predefined time-varying performance ***,simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

关键词： Adaptive tracking control Prescribed performance Input saturation Disturbance observer Neural network

Robust distributed model predictive consensus of discrete-time multi-agent systems:a self-triggered approach

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Frontiers of Information Technology & Electronic Engineering 2021年第8期22卷 1068-1079页

作者： Jiaqi LI Qingling WANG Yanxu SU changyin sun School of Cyber Science and Engineering Southeast UniversityNanjing 210096China School of Automation Southeast UniversityNanjing 210096China

This study investigates the consensus problem of a nonlinear discrete-time multi-agent system(MAS)under bounded additive *** propose a self-triggered robust distributed model predictive control consensus algorithm.A new cost function is constructed and MAS is coupled through this *** on the proposed cost function,a self-triggered mechanism is adopted to reduce the communication ***,to overcome additive disturbances,a local minimum-maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each *** conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop *** each agent,we provide a concrete form of compatibility constraint and a consensus error terminal *** examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.

关键词： Consensus Self-triggered control Distributed model predictive control

Non-fragile switching tracking control for a flexible air-breathing hypersonic vehicle based on polytopic LPV model

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Chinese Journal of Aeronautics 2013年第4期26卷 948-959页

作者： Huang Yiqing sun changyin Qian Chengshan Wang Li College of Electrical Engineering Anhui Polytechnic University School of Automation Southeast University College of Information and Control Nanjing University of Information Science and Technology

This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex nonlinear longitudinal model of the FAHV by using Jacobian linearization and tensor-product （T-P） model transformation approach. Second, for less conservative controller design purpose, the flight envelope is divided into four sub-regions and a non-fragile LPV controller is designed for each parameter sub-region. These non-fragile LPV controllers are then switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a specified performance criterion. The desired non-fragile LPV switching controller is found by solving a convex constraint problem which can be efficiently solved using available linear matrix inequality （LMI） techniques, and robust stability analysis of the closed-loop FAHV system is verified based on multiple Lypapunov functions （MLFs）. Finally, numerical simulations have demonstrated the effectiveness of the proposed approach.

关键词： Hypersonic vehicles Linear parameter varying Non-fragile control Switching control Tracking control

A new self-learning optimal control laws for a class of discrete-time nonlinear systems based on ESN architecture

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Science China(Information Sciences) 2014年第6期57卷 284-293页

作者： SONG RuiZhuo XIAO WenDong sun changyin School of Automation and Electrical Engineering University of Science and Technology Beijing

A novel self-learning optimal control method for a class of discrete-time nonlinear systems is proposed based on iteration adaptive dynamic programming(ADP)*** is proven that the iteration costate functions converge to the optimal one,and a detailed convergence analysis of the iteration ADP algorithm is ***,echo state network(ESN)architecture is used as the approximator of the costate function for each *** ensure the reliability of the ESN approximator,the ESN mean square training error is constrained in the satisfactory *** simulation examples are given to demonstrate that the proposed control method has a fast response speed due to the special structure and the fast training process.

关键词： adaptive dynamic programming discrete-time optimal control ESN costate function

Output feedback control for mobile robot systems with significant external disturbances

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Science China(Information Sciences) 2020年第9期63卷 277-280页

作者： Kang WU Jinya SU changyin sun Key Laboratory of Measurement and Control of CSE Ministry of EducationSchool of Automation Southeast University Department of Aeronautical and Automotive Engineering Loughborough University

Dear editor,The control of nonholonomic systems, like those describing mobile robots, has attracted considerable research attention in recent decades owing to its theoretical and practical importance. Brockett’s theo... 详细信息

关键词： Nonlinear system Nonholonomic system Adaptive control Extended state observer(ESO) Disturbance rejection

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 807-815页

作者： Yuncheng Ouyang Lu Dong Lei Xue changyin sun IEEE the School of Automation and the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

关键词： 2-degree of freedom (DOF) helicopter adaptive control input deadzone integral barrier Lyapunov function neural networks output constraints