中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure

文献类型:期刊论文

作者Luo, Biao1; Liu, Derong2; Wu, Huai-Ning3
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2018-06-01
卷号29期号:6页码:2099-2111
关键词Adaptive Control Adaptive Dynamic Programming Constraints Critic-only Data-based Optimal Control Q-learning
DOI10.1109/TNNLS.2017.2751018
文献子类Article
英文摘要

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition Q((0))(x, a) >= 0. To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

WOS关键词Optimal Tracking Control ; H-infinity Control ; Dynamic-programming Algorithm ; Linear-systems ; Unknown Dynamics ; Policy Iteration ; Neural-networks ; Control Scheme ; Equation ; Update
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000432398300005
资助机构National Natural Science Foundation of China(61503377 ; 61533017 ; 61625302 ; 61473011 ; U1501251)
源URL[http://ir.ia.ac.cn/handle/173211/22045]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
作者单位1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
2.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
3.Beihang Univ, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714
Luo, Biao,Liu, Derong,Wu, Huai-Ning. Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(6):2099-2111.
APA Luo, Biao,Liu, Derong,&Wu, Huai-Ning.(2018).Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(6),2099-2111.
MLA Luo, Biao,et al."Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.6(2018):2099-2111.

入库方式: OAI收割

来源:自动化研究所

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