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Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure
Luo, Biao1; Liu, Derong2; Wu, Huai-Ning3
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2018-06-01
Volume29Issue:6Pages:2099-2111
SubtypeArticle
Abstract

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.

KeywordAdaptive Control Adaptive Dynamic Programming Constraints Critic-only Data-based Optimal Control Q-learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2017.2751018
WOS KeywordOptimal Tracking Control ; H-infinity Control ; Dynamic-programming Algorithm ; Linear-systems ; Unknown Dynamics ; Policy Iteration ; Neural-networks ; Control Scheme ; Equation ; Update
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61503377 ; 61533017 ; 61625302 ; 61473011 ; U1501251)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000432398300005
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22045
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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