检索结果-内蒙古大学图书馆

Event-triggered constrained control using explainable global dual heuristic programming for nonlinear discrete-time systems

引用

NEUROCOMPUTING 2022年第0期468卷 452-463页

作者： Sun, Bo van Kampen, Erik-Jan Delft Univ Technol Dept Control & Operat NL-2629 HS Delft Netherlands

This paper develops an event-triggered optimal control method that can deal with asymmetric input constraints for nonlinear discrete-time systems. The implementation is based on an explainable global dual heuristic programming (XGDHP) technique. Different from traditional GDHP, the required derivatives of cost function in the proposed method are computed by explicit analytical calculations, which makes XGDHP more explainable. Besides, the challenge caused by the input constraints is overcome by the combination of a piece-wise utility function and a bounding layer of the actor network. Furthermore, an event-triggered mechanism is introduced to decrease the amount of computation, and the stability analysis is provided with fewer assumptions compared to most existing studies that investigate event-triggered discrete-time control using adaptive dynamic programming. Two simulation studies are carried out to demonstrate the applicability of the constructed approach. The results present that the developed event-triggered XGDHP algorithm can substantially save the computational load, while maintain comparable performance with the time-based approach. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Event-triggered control Global dual heuristic programming Asymmetric input constraints Explainable artificial intelligence adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Event-triggered adaptive integral reinforcement learning method for zero-sum differential games of nonlinear systems with incomplete known dynamics

引用

NEURAL COMPUTING & APPLICATIONS 2022年第13期34卷 10775-10786页

作者： Liu, Pengda Zhang, Huaguang Sun, Jiayue Tan, Zilong Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China

This paper designs a novel event-based adaptive learning method for solving zero-sum games (ZSGs) of nonlinear systems with incomplete known dynamics. Firstly, a discounted cost is introduced for the system with nonzero equilibrium point to obtain the near-optimal strategy pair. Then, the employment of integral reinforcement learning (IRL) makes it unnecessary to acquire the model of the drift dynamics. To approximate the solution of the Hamilton-Jacobi-Isaacs equations (HJIEs), single-critic network is constructed with the modified tuning law utilizing preprocessed data. For purpose of increasing algorithm efficiency, the event-triggered mechanism (ETM) is introduced which could obviate Zeno behavior. Furthermore, the state and critic weight vector error are proved to be uniform ultimate bounded (UUB) through Lyapunov approach. Finally, the effectiveness of the proposed method is validated by conducting a simulation experiment.

关键词： Event-triggered control adaptive dynamic programming adaptive critic design Integral reinforcement learning Zero-sum games

来源：评论

学校读者我要写书评

暂无评论

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

引用

Control Theory and Technology 2022年第1期20卷 1-19页

作者： Weinan Gao Zhong-Ping Jiang Department of Mechanical and Civil Engineering College of Engineering and Science Florida Institute of Technology150 W.University Blvd.MelbourneFL32901USA Department of Electrical and Computer Engineering Tandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance *** proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control *** this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven *** will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

关键词： adaptive optimal output regulation adaptive dynamic programming Reinforcement learning Learning-based control

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Safe Control for the Zero-Sum Game of Nonlinear Safety-Critical Systems With Input Saturation

引用

IEEE ACCESS 2022年 10卷 40324-40337页

作者： Qin, Chunbin Zhu, Heyang Wang, Jinguang Xiao, Qiyang Zhang, Dehua Henan Univ Sch Artificial Intelligence Zhengzhou 450000 Peoples R China

In this paper, a novel adaptive dynamic programming (ADP)-based event-triggered safe control method is proposed to solve the zero-sum game problem of nonlinear safety-critical systems with safety constraints and input saturation. First, the barrier function-based system transformation, the zero-sum game problem with safety constraints and input saturation is transformed into an equivalent input saturation zero-sum game problem, so as to guarantee that the system does not violate the safety constraints. Furthermore, the non-quadratic utility function is introduced into the performance function to solve input saturation. Then, a critic neural network (NN) is constructed to approximate the optimal safety value function. Subsequently, a novel event-triggered scheme is developed to determine the update instant of the control law and the disturbance law. Therefore, the proposed ADP-based event-triggered safe control method can ensure that the states of nonlinear safety-critical systems satisfy the safety constraints, while greatly reducing the amount of calculation and saving communication resources. In addition, during the learning process, the concurrent learning is used to relax the persistence of excitation (PE) condition. According to the Lyapuov theory, it is proved that the weight estimation error of the critic neural network and the states are uniformly ultimately bounded (UUB), and the Zeno behavior is excluded. Finally, a simulation example verifies the effectiveness of the proposed method.

关键词： Safety Game theory Games Nonlinear dynamical systems Nash equilibrium Mathematical models Licenses adaptive dynamic programming barrier function event-triggered control input saturation safety constraints zero-sum game

来源：评论

学校读者我要写书评

暂无评论

Online Value Iteration for Intelligent Discounted Tracking Design of Constrained Systems

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第9期69卷 3829-3833页

作者： Wang, Ding Wu, Junlong Ren, Jin Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing Key Lab Computat Intelligence & Intellige Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

In this brief, in order to solve the discounted optimal tracking control problem for discrete-time systems with control constraints, an advanced online value iteration (VI) algorithm is developed. First, we revisit discounted general value iteration (GVI) for optimal tracking control design. Second, the stability condition of GVI is established, which can be applied to evaluate the current iterative tracking control policy. Third, based on the concept of attraction domain, the evolving tracking control policies generated by online VI can be obtained through judging the location of the current tracking error. Finally, we prove system stability based on online VI for the present tracking control problem. A simulation example is shown to illustrate the effectiveness of the developed algorithm.

关键词： Stability criteria Cost function Trajectory Nonlinear systems Mathematical models dynamic programming Control design adaptive dynamic programming attraction domain control constraints online value iteration optimal tracking control stability proof

来源：评论

学校读者我要写书评

暂无评论

Reinforcement learning-based optimal fault-tolerant control for offshore platforms

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2022年第6期236卷 1187-1196页

作者： Ziaei, Amin Kharrati, Hamed Salim, Mina Rahimi, Afshin Univ Tabriz Fac Elect & Comp Engn Tabriz *** Iran Univ Windsor Dept Mech Automot & Mat Engn Windsor ON Canada

This article investigates a novel reinforcement learning-based fault-tolerant control approach for steel-jacket offshore platforms. In the first step, the dynamic of the steal-jacket offshore platform with an active mass damper is considered, and the equivalent linear time-invariant model is obtained with the actuator fault. In fault-free conditions, an optimal controller is designed to keep the system stable under external wave force. Subsequently, in faulty conditions, the actuator fault is estimated by the fault observer. Next, by inserting the actuator fault estimation into the cost function, the fault-tolerant control problem transforms into the optimal control problem. The online policy iteration is used to minimize the new cost function. Finally, the final control law, which is a mixture of the nominal and the modified control law, stabilizes the offshore platform and improves its performance in the presence of the actuator fault without needing the complete knowledge of the offshore platform. The simulation results show the effectiveness of the proposed method.

关键词： Steel-jacket offshore platforms fault-tolerant control fault observer reinforcement learning adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Event-triggered optimal control for discrete-time multi-player non-zero-sum games using parallel control

引用

INFORMATION SCIENCES 2022年 584卷 519-535页

作者： Lu, Jingwei Wei, Qinglai Wang, Ziyang Zhou, Tianmin Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

This paper presents a novel event-triggered optimal control (ETOC) method for discrete -time (DT) multi-player non-zero-sum games (NZSGs). First, a novel event-triggered algo-rithm is developed for DT multi-player NZSGs based on the time-triggered optimal value functions. Therefore, the developed event-triggered algorithm only needs to solve the time-triggered Hamilton-Jacobi-Bellman (HJB) equations. Then, the asymptotic stability of the closed-loop system is proved. Additionally, we show that an upper bound for the sum of the actual performance indices of all the players can be determined in advance. A key step in the implementation of the developed event-triggered algorithm is to obtain the next state of the actual system, which is difficult to implement on the actual system. Thus, a parallel control method is utilized to predict the next state by constructing the par-allel system for the actual system and obtain the optimal value functions. The method that combines the developed event-triggered algorithm and parallel control is called the event-triggered optimal parallel control (ETOPC) method. The neural network (NN) technique and the iterative adaptive dynamic programming (IADP) technique are employed in parallel control. Moreover, the control stability is shown further in the consideration of the NN weight approximation errors. Finally, two simulations justify the theoretical conjectures. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Event-triggered Non-zero-sum games Parallel control Neural network adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

adaptive critic designs for decentralised robust control of nonlinear interconnected systems via event-triggering mechanism

引用

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2022年第5期53卷 1031-1047页

作者： Luo, Qiliang Xue, Shan Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this article, a novel decentralised event-triggered control scheme is developed for the robust stabilisation of interconnected systems. The existing literature about decentralised robust control by adaptive critic designs (ACDs) is mostly time-triggered which requires extensive communication and computation. To extend the ACD framework and save the communication and computational resources, the event-triggered decentralised control using ACDs is investigated. First, we utilise a series of auxiliary subsystems to restructure the interconnected systems in a distributed way, so as to transform the robust control problem into the corresponding optimal control problem. Then, adaptive critic designs with event-triggering mechanism are investigated to solve the Hamilton-Jacobi-Bellman equations via local policy iteration algorithm. The single-critic networks are employed to achieve the approximation of optimal value functions. The Lyapunov stability proof ensures the asymptotic stability of the whole system and the uniform ultimate boundedness of the critic neural networks. Finally, an interconnected plant is employed to demonstrate the effectiveness of proposed method.

关键词： adaptive critic designs adaptive dynamic programming event-triggered control interconnected systems neural networks robust stabilisation

来源：评论

学校读者我要写书评

暂无评论

Offline and Online adaptive Critic Control Designs With Stability Guarantee Through Value Iteration

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13262-13274页

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

This article is concerned with the stability of the closed-loop system using various control policies generated by value iteration. Some stability properties involving admissibility criteria, the attraction domain, and so forth, are investigated. An offline integrated value iteration (VI) scheme with a stability guarantee is developed by combining the advantages of VI and policy iteration, which is convenient to obtain admissible control policies. Also, based on the concept of attraction domain, an online adaptive dynamic programming algorithm using immature control policies is developed. Remarkably, it is ensured that the state trajectory under the online algorithm converges to the origin. Particularly, for linear systems, the online ADP algorithm with a general scheme possesses more enhanced stability property. The theoretical results reveal that the stability of the linear system can be guaranteed even if the control policy sequence includes finite unstable elements. The numerical results verify the effectiveness of the present algorithms.

关键词： Stability criteria Asymptotic stability Numerical stability Power system stability Heuristic algorithms Cost function Trajectory adaptive dynamic programming asymptotic stability online adaptive critic control policy iteration (PI) reinforcement learning (RL) value iteration (VI)

来源：评论

学校读者我要写书评

暂无评论

Multiplayer zero-sum games optimal control for modular robot manipulators with interconnected dynamic couplings

引用

INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2022年第12期36卷 3254-3270页

作者： Zhu, Xinye An, Tianjiao Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

This article presents a multiplayer zero-sum game optimal control for modular robot manipulators (MRMs) via adaptive dynamic programming (ADP). The dynamic model of MRMs with interconnected dynamic couplings (IDCs) is formulated. The control law and IDC effect are regarded as players in each module. The optimal tracking control problem of the MRM system can be transformed into a multiplayer zero-sum game. Base on ADP algorithm, the Hamilton-Jacobi-Issacs (HJI) equation is solved via critic neural network. Then, the optimal control law can be derivated. The closed-loop robotic system is proved to be asymptotic stable and experiments are conducted to verify the effectiveness of the control method.

关键词： adaptive dynamic programming modular robot manipulators multiplayer zero-sum game optimal control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：