检索结果-南通市图书馆

A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1288-1303页

作者： chaoxu mu Yong Zhang Guangbin Cai Ruijun Liu Changyin Sun the School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China the College of Missile Engineering Rocket Force University of EngineeringXi’an 710025China the School of Computer and Information Engineering Beijing Technology and Business UniversityBeijing 100048China the School of Automation Southeast UniversityNanjing 210096China

In this paper,a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear systems.Motivated by the classical on-policy and off-policy algorithms of reinforcement learning,the online feedback relearning(FR)algorithm is developed where the collected data includes the influence of disturbance signals.The FR algorithm has better adaptability to environmental changes(such as the control channel disturbances)compared with the off-policy algorithm,and has higher computational efficiency and better convergence performance compared with the on-policy algorithm.Data processing based on experience replay technology is used for great data efficiency and convergence stability.Simulation experiments are presented to illustrate convergence stability,optimality and algorithmic performance of FR algorithm by comparison.

关键词： Data episodes experience replay neural networks reinforcement learning(RL) uncertain systems

Adaptive composite frequency control of power systems using reinforcement learning

在线全文

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2022年第4期7卷 671-684页

作者： chaoxu mu Ke Wang Shiqian Ma Zhiqiang Chong Zhen Ni School of Electrical and Information Engineering Tianjin UniversityTianjinChina State Grid Tianjin Electric Power Company Electric Power Research Institute TianjinChina Department of Electrical Engineering and Computer Science Florida Atlantic UniversityBoca RatonFLUSA

With the incorporation of renewable energy,load frequency control(LFC)becomes more challenging due to uncertain power generation and changeable load demands.The electric vehicle(EV)has been a popular transportation and can also provide flexible options to play a role in frequency regulation.In this paper,a novel adaptive composite controller is designed to solve the LFC problem for the interconnected power system with electric vehicles and wind turbine.EVs are used as regulation resources to effectively compensate the power mismatch.First,the sliding mode controller is developed to reduce the random influences caused by the wind turbine generation system.Second,an auxiliary controller with reinforcement learning is proposed to produce adaptive control signals,which will be attached to the primary proportion-integration-differentiation control signal in a realtime manner.Finally,by considering random wind power,load disturbances and output constraints,the proposed scheme is verified on a two-area power system under four different cases.Simulation results demonstrate that the proposed adaptive composite frequency control scheme has a competitive performance with regard to dynamic performance.

关键词： power composite attached

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

扰动线性多智能体系统的分布式资源分配算法

中国科学：信息科学 2024年第4期54卷 911-926页

作者：时侠圣孙长银穆朝絮安徽大学自主无人系统技术教育部工程研究中心合肥230601 安徽大学安徽省无人系统与智能技术工程研究中心合肥230601 安徽大学人工智能学院合肥230601 天津大学电气与自动化信息工程学院天津300192

针对一般异构线性多智能体系统的分布式资源分配问题,结合智能体动力学特性设计一类分布式优化算法,旨在实现扰动抑制的同时完成资源的最优分配.面对扰动模型已知干扰信号,本文利用智能体状态信息设计一种自适应分布式优化算法,其中拉... 详细信息

针对一般异构线性多智能体系统的分布式资源分配问题,结合智能体动力学特性设计一类分布式优化算法,旨在实现扰动抑制的同时完成资源的最优分配.面对扰动模型已知干扰信号,本文利用智能体状态信息设计一种自适应分布式优化算法,其中拉格朗日(Lagrange)乘子对偶变量控制参数自动增加,直至拉格朗日乘子对偶变量达到一致.当智能体状态信息不可测时,本文将上述算法修改为基于输出反馈的分布式优化算法.当全局成本函数强凸时,借助拉萨尔(LaSalle)不变性引理证明上述所提算法渐近收敛到全局最优解.面对未知扰动信号,本文利用扩张状态观测器技术设计一种分布式优化算法.当未知扰动信号为常数或其微分趋于零时,利用李雅普诺夫(Lyapunov)理论证明上述算法渐近收敛至全局最优解.最后利用数值仿真验证上述算法有效.

关键词：异构多智能体系统资源分配自适应通信权重状态反馈输出反馈未知扰动

Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism

在线全文

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2017年第5期60卷 252-254页

作者： Ding WANG chaoxu mu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin University

Optimal feedback design of dynamical systems is a significant topic in automatic control community and information science.As for nonlinear systems,optimal control design always leads to coping with the nonlinear Hamilton-Jacobi-Bellman equation.Nevertheless,it is intractable to acquire the analytic solution of the nonlinear Hamilton-JacobiBellman equation for general nonlinear systems.

关键词： Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism

Neural network-based adaptive decentralized learning control for interconnected systems with input constraints

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Control Theory and Technology 2021年第3期19卷 392-404页

作者： chaoxu mu Hao Luo Ke Wang Changyin Sun School of Electrical and Information Engineering Tianjin UniversityTianjin300072China School of Automation Southeast UniversityNanjing210096China

In this paper,the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints.Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem,the decentralized control strategy can be established by a series of optimal control policies.A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem.This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function,respectively.The additional stabilizing term is introduced and an improved weight updating law is derived,which relaxes the requirement of initial admissible control policy.Besides,the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints.The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory.Finally,the effectiveness of the proposed decentralized learning control method is verified by simulation results.

关键词： Decentralized control Actor-critic learning Neural network Input constraints

Learning-based control for discrete-time constrained nonzero-sum games

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2021年第2期6卷 203-213页

作者： chaoxu mu Jiangwen Peng Yufei Tang School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Computer Electrical Engineering and Computer ScienceFlorida Atlantic UniversityUSA

A generalized policy-iteration-based solution to a class of discrete-time multi-player nonzero-sum games concerning the control constraints was proposed.Based on initial admissible control policies,the iterative value function of each player converges to the optimum approximately,which is structured by the iterative control policies satisfying the Nash equilibrium.Afterwards,the stability analysis is shown to illustrate that the iterative control policies can stabilize the system and minimize the performance index function of each player.Meanwhile,neural networks are implemented to approximate the iterative control policies and value functions with the impact of control constraints.Finally,two numerical simulations of the discrete-time two-player non-zero-sum games for linear and non-linear systems are shown to illustrate the effectiveness of the proposed scheme.

关键词： iterative nonzero approximate

Heuristic dynamic programming-based learning control for discrete-time disturbed multi-agent systems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Control Theory and Technology 2021年第3期19卷 339-353页

作者： Yao Zhang chaoxu mu Yong Zhang Yanghe Feng School of Electrical and Information Engineering Tianjin UniversityTianjin300072China College of Systems Engineering National University of Defense TechnologyChangsha410073HunanChina

Owing to extensive applications in many fields,the synchronization problem has been widely investigated in multi-agent systems.The synchronization for multi-agent systems is a pivotal issue,which means that under the designed control policy,the output of systems or the state of each agent can be consistent with the leader.The purpose of this paper is to investigate a heuristic dynamic programming(HDP)-based learning tracking control for discrete-time multi-agent systems to achieve synchronization while considering disturbances in systems.Besides,due to the difficulty of solving the coupled Hamilton–Jacobi–Bellman equation analytically,an improved HDP learning control algorithm is proposed to realize the synchronization between the leader and all following agents,which is executed by an action-critic neural network.The action and critic neural network are utilized to learn the optimal control policy and cost function,respectively,by means of introducing an auxiliary action network.Finally,two numerical examples and a practical application of mobile robots are presented to demonstrate the control performance of the HDP-based learning control algorithm.

关键词： multi-agent systems Heuristic dynamic programming(HDP) Learning control Neural network Synchronization

Characteristic Model-based Discrete-time Sliding Mode Control for Spacecraft with Variable Tilt of Flexible Structures

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2016年第1期3卷 42-50页

作者： Lei Chen Yan Yan chaoxu mu Changyin Sun School of Automation Southeast University Department of Electrical Engineering and Automation Tianjin University

In this paper, the finite-time attitude tracking control problem for the spacecrafts with variable tilt of flexible appendages in the conditions of exogenous disturbances and inertia uncertainties is addressed. First the characteristic modeling method is applied to the problem of the spacecraft modeling.Second, a novel adaptive sliding mode surface is designed based on the characteristic model. Furthermore, a discrete-time sliding mode control(DTSMC) law, which makes the tracking error converge into a predefined bound in finite time, is proposed by employing the parameters of characteristic model associated with the sliding mode surface to provide better performances,robustness, faster response, and higher control precision. The designed DTSMC includes the adaptive control architecture and is chattering-free. Finally, digital simulations of a sun synchronous orbit satellite(SSOS) are presented to illustrate effectiveness of the control strategies as well as to verify the practical feasibility of the rapid maneuver mission.

关键词： Spacecraft attitude maneuver characteristic model sliding mode control finite time

Data-based intelligent modeling and control for nonlinear systems

在线全文

学校读者我要写书评

暂无评论

Frontiers of Electrical and Electronic Engineering in China 2011年第2期6卷 291-299页

作者： chaoxu mu Changyin SUN School of Automation Southeast UniversityNanjing 210096China

With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested.

关键词： offline and online data intelligent modeling data-based control perspective