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检索条件"主题词=Adaptive Dynamic Programming"

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ADP-Based Model Reference adaptive Control Design for Unknown Discrete-Time Nonlinear Systems 37

ADP-Based Model Reference Adaptive Control Design for Unknow...

引用

37th Chinese Control Conference (CCC)

作者： Wang, Wei Chen, Xin Wang, Fang Fu, Hao China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China

ISBN: (纸本)9789881563958

In engineering applications, it is very useful to make a nonlinear system behave like a linear stable system. However, how to design a model reference adaptive control (MRAC) as the nonlinear dynamics is unknown is the key problem. In this study, an MRAC with adaptive dynamic programming (ADP) algorithm is presented for discrete-time nonlinear unknown dynamic systems, in which a multi-layer neural network (NN) model is utilized to describe the nonlinear system's dynamics. Then based on this approximate model, a feedforward neuro-controller is developed as the desired control corresponding to the reference input. Third, the iterative ADP algorithm, in the form of actor-critic framework, employs two NNs to estimate the action value function, and generates the feedback control which, together with the feedforward neuro-controller, makes the state of the system track the reference model. Finally the feasibility of the new approach is verified by two numerical experiments.

关键词： Model reference adaptive control adaptive dynamic programming Nonlinear system Q-function

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Nearly Optimal Consensus Control of Discrete Time Multiagent Systems with Time Delays

Nearly Optimal Consensus Control of Discrete Time Multiagent...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zhang, Yao Mu, Chaoxu Zhao, Qian Wang, Ke Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9781728124858

This paper researches the optimal consensus control of discrete time multiagent systems with time delays by applying the heuristic dynamic programming (HDP) iteration algorithm. It is known that the agent can only accept the message from itself and its neighboring agents. The communication among the agents is modeled by using the communication structure graph. The objective is to make all the followers track the desired leader. First, the discrete time multiagent systems with time delays are transformed into systems without time delays. Then the HDP algorithm is used to solve the multiagent consensus control problem. Neural networks are applied to approximate the performance index and the optimal control policy. At last, a simulation study is presented to verify the theoretical conclusion.

关键词： Multiagent systems neural networks consensus control adaptive dynamic programming

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Approximate Optimal Sliding Mode Tracking Control for Modular Reconfigurable Robots Based on Critic-only Structure 3rd

Approximate Optimal Sliding Mode Tracking Control for Modula...

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3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS)

作者： Xia, Hongbing Xia, Fuquan Zhao, Bo Huang, Yinghui Bengbu Univ Sch Elect & Elect Engn Bengbu 233030 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Bengbu Univ Sch Sci Bengbu 233030 Peoples R China

In this paper, an approximate optimal sliding mode tracking control (SMTC) strategy is investigated for modular reconfigurable robots (MRRs) through critic-only structure-based adaptive dynamic programming (ADP) scheme. The SMTC is achieved by three parts, i.e., the optimal control of the nominal system, the sliding mode-based iterative control and an adaptive robust term. The sliding mode-based iterative controller suppresses the error caused by the trajectory tracking, and the adaptive robust term is employed to ensure the reachable condition of sliding mode surface. By solving the Hamilton-Jacobi-Bellman equation with the critic neural network only, the sliding mode-based iterative control can be derived. The SMTC strategy can drive the MRR to present achieve a faster control action based on the approximate optimal control. The closed-loop MRR system is guaranteed to be asymptotically stable under the developed SMTC policy. At last, the effectiveness of the presented strategy was validated via the comparative simulation. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming Sliding mode tracking control Neural networks Modular reconfigurable robots

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Control and Communication Co-design Framework for Voltage/Var Control in Distribution Network 35

Control and Communication Co-design Framework for Voltage/Va...

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35th Chinese Control Conference (CCC)

作者： He, Jiasheng Chen, Cailian Zhu, Shanying Guan, Xinping Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9789881563910

Today's power systems are in transformation process to a smarter grid via integration of distributed generation and intelligent loads, where advanced information and communication technology provides more reliable and efficient communication links. For purpose of dealing with the increasing dynamic parts of the smart grid and its corresponding cyber systems, we propose a control and communication co-design framework for Voltage/Var control (VVC) in distribution network, which consists of a communication protocol and an adaptive dynamic programming (ADP) based algorithm. To minimize voltage deviation and communication cost, we extend control interval by integration of an event-triggered mechanism. The effectiveness of proposed algorithm is demonstrated by a simulation using Matlab+NS-3 co-simulation in a distribution network.

关键词： adaptive dynamic programming Commnunication protocol Distributed generation Event-triggerd Kalman filter Voltage/var control

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adaptive Optimal Control Based on Parameter Estimation for Servomechanisms

Adaptive Optimal Control Based on Parameter Estimation for S...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Zhang, Chi Gan, Minggang Chen, Jie Chen, Chen Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing Peoples R China

In this paper, an adaptive optimal control scheme based on parameter estimation is proposed for optimal control problem of servomechanisms with unknown parameters. Parameters of the servomechanism dynamics are identified online first. Based on parameter estimation, the optimal control policy is achieved through adaptive dynamic *** estimation removes an approximation structure from the original method. Therefore the number of unknown weights in approximation structures, to be calculated, is reduced. The analysis for convergence is presented to prove the validity. Finally, simulation results demonstrate that the scheme can approximate the optimal control policy rapidly and effectively. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： servomechanism optimal control parameter estimation adaptive dynamic programming

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An Event-Triggered ADP Controller for Single Link Robot Arm System Based on Output Position 30

An Event-Triggered ADP Controller for Single Link Robot Arm ...

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30th Chinese Control and Decision Conference (CCDC)

作者： Yang, Yang Xu, Chuang Meng, Qing Tan, Jie Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China

ISBN: (纸本)9781538612446

In this paper, an event -triggered adaptive dynamic programming (ADP) control method is proposed for a single link robot ann system based on the output position. Firstly, we propose a state observer with the neural network (NN) technology to reconstruct the signals of the angular position and angular velocity of the robot arm. Then, in order to solve the optimal control problem of the robot arm system, an ADP controller is introduced in an event-triggered manner based on the inner states from the observer, where the ADP method exploits neural network to approximate the performance index in the system and the event -triggered mechanism is designed to update the control signal aperiodically for the reduction of the computation and transmission load. Finally, as for the continuous and the jump dynamics, the uniform ultimate boundedness (ULM) of the closed-loop system is guaranteed with the help of Lyapunov theory, and simulation results are presented to demonstrate the effectiveness of the proposed controller.

关键词： adaptive dynamic programming Event-Triggered Approach Output Control

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Distributed ADP-based Finite-time optimal control for Interconnected Multi-machine Power Systems with Switching Topology 10

Distributed ADP-based Finite-time optimal control for Interc...

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10th International Conference on Control, Automation and Robotic (ICCAR)

作者： Zhang, Longjie Chen, Yong Abdelhameed, Esam Hafez Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sichuan Prov Engn Technol Res Ctr Elect Vehicle D Chengdu 611731 Peoples R China Aswan Univ Fac Energy Engn Aswan 81711 Egypt

ISBN: (纸本)9798350373172;9798350373189

This paper concentrate on the finite-time optimal control for the interconnected power systems with switching communication topology. The finite-time ADP-based optimal controller (FTADPOC) is proposed for the finite-time stability of the interconnected power systems (IPSs). Firstly, the consensus error model with switching communication topology of the IPSs is given. Then, the finite-time optimal controller solved by the HJB equation with the value function in the finite-time stability space is constructed. Furthermore, based on the finite-time optimization theory, the finite-time critic network for the approximation of the value function is implemented to solve the optimal controller, and the corresponding finite-time convergence of the proposed tuning law is illustrated. Finally, the result analysis on the interconnected power systems illustrates the effectiveness of the proposed FTADPOC.

关键词： adaptive dynamic programming Optimal control Finite-time critic network Interconnected power systems

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Event-Triggered Optimal Safety Control for Nonlinear Safety-Critical Systems with Disturbance 34

Event-Triggered Optimal Safety Control for Nonlinear Safety-...

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34th Chinese Control and Decision Conference (CCDC)

作者： Zhu, Heyang Wang, Jinguang Zhang, Jishi Zhang, Dehua Xiao, Qiyang Qin, Chunbin Henan Univ Sch Artificial Intelligence Zhengzhou 450000 Peoples R China Henan Univ Sch Software Kaifeng 475000 Peoples R China

ISBN: (纸本)9781665478960

In this paper, the event-triggered optimal safety control method is proposed to solve the zero-sum game problem of nonlinear safety-critical systems with disturbance. First, we transform the safety-critical system with safety constraints into an equivalent system without safety constraints by using the barrier function. Then, for relieving the computation pressure and saving communication cost, the event-triggered mechanism is introduced, and a safe event-triggered condition is presented, meanwhile, the Zeno behavior is excluded. In addition, only a critic neural network (NN) is used to implement the proposed method. During the learning process, the past data and current data are used to relax the persistence of excitation (PE) condition. According to the Lyapunov theory, it can be proved that the states and the weight estimation error of critic NN are uniformly ultimately bounded (UUB). Finally, a simulation example shows the effectiveness of the proposed method.

关键词： adaptive dynamic programming Barrier function Event-triggered control Neural network Safety

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Fault Tolerant Tracking Control Through Particle Swarm Optimization Based Policy Iteration 35

Fault Tolerant Tracking Control Through Particle Swarm Optim...

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35th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Liu, Xi Liu, Derong Zhao, Bo Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China

ISBN: (纸本)9781728176840

This paper focuses on fault tolerant tracking control (FTTC) problems for nonlinear systems with actuator failure. For fault-free system, the tracking control input is derived by the policy iteration. To deal with the difficulty in choosing the weight of critic neural network (CNN), the CNN is trained by the particle swarm optimization to instead the traditional gradient descent method. To handle the actuator failure, a fault observer is constructed to compensate the tracking control input, and then the fault tolerant tracking controller is derived. The developed FTTC scheme can guarantee the tracking errors to be uniformly ultimately bounded even if the system suffers form actuator faults. A simulation study is provided to illustrate the effectiveness of the designed FTTC scheme.

关键词： adaptive dynamic programming Reinforcement learning Fault tolerant control Optimal control Neural networks Particle swarm optimization

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Event-based Optimal Regulator Design for Nonlinear Networked Control Systems

Event-based Optimal Regulator Design for Nonlinear Networked...

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IEEE Symposium on adaptive dynamic programming and Reinforcement Learning (ADPRL)

作者： Sahoo, Avimanyu Xu, Hao Jagannathan, S. Missouri Univ Sc & Tech Dept Elect & Comp Engn Rolla MO 65409 USA Texas A&M Univ Coll Sci & Engn Dept Elect Engn Corpus Christi TX USA

ISBN: (纸本)9781479945528

This paper presents a novel stochastic event-based near optimal control strategy to regulate a networked control system (NCS) represented as an uncertain nonlinear continuous time system. An online stochastic actor-critic neural network (NN) based approach is utilized to achieve the near optimal regulation in the presence of network constraints, such as, network induced time-varying delays and random packet losses under event-based transmission of the feedback signals. The transformed nonlinear NCS in discrete-time after the incorporation the delays and packet losses is utilized for the actor-critic NN based controller design. To relax the knowledge of the control coefficient matrix, a NN based identifier is used. Event sampled state vector is utilized as NN inputs and their respective weights are updated non-periodically at the occurrence of events. Further, an event-trigger condition is designed by using the Lyapunov technique to ensure ultimate boundedness of all the closed-loop signals and save network resources and computation. Moreover, policy and value iterations are not utilized for the stochastic optimal regulator design. Finally, the analytical design is verified by using a numerical example by carrying out Monte-Carlo simulations.

关键词： Event-triggered control optimal control adaptive dynamic programming neural networks networked control systems

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