检索结果-南通市图书馆

Heterogeneous multi-player imitation learning

在线全文

学校读者我要写书评

暂无评论

Control Theory and Technology 2023年第3期21卷 281-291页

作者： Bosen lian Wenqian Xue frank l.lewis Department of Electrical and Computer Engineering Auburn UniversityAuburnAL 36849USA State Key laboratory of Synthetical Automation for Process Industries and International Joint Research laboratory of Integrated Automation Northeastern UniversityShenyang 110819LiaoningChina UTA Research Institute University of Texas at Arlington(UTA)Fort WorthTX 76118USA

This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input *** propose a model-free data-driven inverse reinforcement learning(Rl)algorithm for a leaner to find the cost functions of a N-player Nash expert system given the expert's states and control *** allows us to address the imitation learning problem without prior knowledge of the expert's system *** achieve this,we provide a basic model-based algorithm that is built upon Rl and inverse optimal *** serves as the foundation for our final model-free inverse Rl algorithm which is implemented via neural network-based value function *** analysis and simulation examples verify the methods.

关键词： Imitation learning Inverse reinforcement learning Heterogeneous multi-player games Data-driven model-free control

Reinforcement learning for optimal tracking of large-scale systems with multitime scales

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2023年第7期66卷 5-29页

作者： Jinna lI Hao NIE Tianyou CHAI frank l.lewis School of Information and Control Engineering Liaoning Petrochemical University State Key laboratory of Synthetical Automation for Process Industries Northeastern University UTA Research Institute University of Texas at Arlington

This paper aims to solve an optimal tracking control(OTC) problem of large-scale systems with multitime scales and coupled subsystems using singular perturbation(SP) theory and reinforcement learning(Rl) techniques. A considerable contribution of this paper is the development of a data-driven SP-based Rl method for the OTC of unknown large-scale systems with multitime scales. To achieve this, a multitime scale tracking problem was decomposed into a linear quadratic tracker problem for slow subsystems and a dynamical game problem for fast subsystems using the SP theory. Then, the distributed composite feedback controllers were found using a distributed off-policy integral Rl algorithm that uses only measured data from the system in real time. Thus, the operational index can follow its prescribed target value via an approximately optimal approach. Theoretical analysis and proof are presented to demonstrate that the sum of the performances of reduced-order subsystems is approximately equal to the performance of the original large-scale system. Finally, numerical and practical examples are provided to validate the effectiveness of the proposed method.

关键词： reinforcement learning singular perturbation multitime scales data-driven control

Adaptive Uniform Performance Control of Strict-Feedback Nonlinear Systems With Time-Varying Control Gain

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song frank l.lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

Error-based adaptive optimal tracking control of nonlinear discrete-time systems

在线全文

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第1期67卷 154-167页

作者： Chun lI Jinliang DING frank l.lewis Tianyou CHAI State Key laboratory of Synthetical Automation for Process Industries Northeastern University UTA Research Institute The University of Texas at Arlington

In this paper,for the output tracking problem of nonlinear discrete-time systems,a performance index is newly defined using the adaptive dynamic programming(ADP) technique to completely eliminate tracking errors in *** contrast to traditional definitions of performance indices in other ADP-based methods,the proposed performance index is not only designed from the perspective of output tracking errors but also introduced errors of system states and control inputs at adjacent stages,which is suitable for practical situations in many industrial applications,such as the alumina production,the flotation process,and the mineral grinding *** proved that the obtained controller can make the system output fully track the given reference trajectory by applying the iterative criteria of the ADP *** addition,the proposed algorithm was implemented using a data-driven technique and neural networks to avoid analyzing and deducing the complicated dynamics of actual industrial ***,using historical data for a forced-circulation evaporation system in the alumina production process,the effect of the proposed approach was verified through a numerical simulation and compared with that of the proportional-integral controller.

关键词： adaptive dynamic programming iterative criteria performance index tracking errors tracking problem

同方期刊数据库

Stochastic DoS Attack Allocation Against Collaborative Estimation in Sensor Networks

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1225-1234页

作者： Ya Zhang lishuang Du frank l.lewis School of Automation Southeast Universityand also with the Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationNanjing 210096China University of Texas at Arlington Research Institute Fort WorthTX 76118 USA IEEE

In this paper,denial of service(DoS)attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is *** the communication channels between sensors and a remote estimator,the attacker chooses some channels to randomly jam DoS attacks to make their packets randomly dropped.A stochastic power allocation approach composed of three steps is ***,the minimum number of channels and the channel set to be attacked are ***,a necessary condition and a sufficient condition on the packet loss probabilities of the channels in the attack set are provided for general and special systems,***,by converting the original coupling nonlinear programming problem to a linear programming problem,a method of searching attack probabilities and power to minimize the attack energy is *** effectiveness of the proposed scheme is verified by simulation examples.

关键词： Attack allocation denial of service(DoS)attack packet loss remote estimation sensor networks

Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

在线全文

学校读者我要写书评

暂无评论

控制理论与应用（英文版） 2007年第1期5卷 1-11页

作者： frank l.lewis Automation and Robotics Research Institute University of TexasArlington TX 76118USA

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded l2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.

关键词： Constrained input system Hamilton-Jacobi-Isaacs H-infinity control Finite-horizon zero-sum games Neural network control

Recent Progress in Reinforcement learning and Adaptive Dynamic Programming for Advanced Control Applications

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong liu Jinna li frank l.lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(Rl) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with Rl and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and Rl frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to Rl and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and Rl for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(Rl)

Consensus controller for multi-UAV navigation

在线全文

学校读者我要写书评

暂无评论

Control Theory and Technology 2018年第2期16卷 110-121页

作者： Patrik KOlARIC Ci CHEN Ankur DAlAl frank l. lewis UTA Research Institute University of Texas at Arlington 7300 Jack Newell Bird S Fort Worth TX 76118 U.S.A. School of Automation Guangdong University of Technology Guangdong Key laboratory of loT Information Technology Guangzhou Guangzhou 510006 China State Key laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning 110819 China

In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of lyapunov theory, it is shown that all signals in the closed-loop multi- UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.

关键词： Consensus control multi-agent system quadrotor lyapunov stability distributed system

A unified approach to output synchronization of heterogeneous multi-agent systems via l2-gain design

维普期刊数据库评论

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 2017年第4期15卷 340-353页

作者： Shan Zuo Yongduan Song Hamidreza Modares frank l. lewis Ali Davoudi 1. School of Automation Engineering University of Electronic Science and Technology of China Chengdu Sichuan 611731 China 2. The University of Texas at Arlington Research Institute Fort Worth TX 76118 U.S.A.

In this paper, a unified design procedure is given for output synchronization of heterogeneous multi-agent systems （MAS） on communication graph topologies, using relative output measurements from neighbors. Three different control protocols, namely, full-state feedback, static output-feedback, and dynamic output-feedback, are designed for output synchronization. It is seen that a unified design procedure for heterogeneous MAS can be given by formulation and solution of a suitable local l2-gain design problem. Sufficient conditions are developed in terms of stabilizing the local agents＇ dynamics, satisfying a certain small-gain criterion, and solving the output regulator equations. local design procedures are presented for each agent to guarantee that these sufficient conditions are satisfied. The proposed control protocols require only one copy of the leader＇s dynamics in the compensator, regardless of the dimensions of the outputs. This results in lower-dimensional compensators for systems with high-order outputs, compared to the p-copy internal model approach. All three proposed control protocols are verified using numerical simulations.

关键词： Heterogeneous systems output-feedback output synchronization small-gain theorem