Adaptive Critic Nonlinear Robust Control: A Survey
Wang, Ding1,2,3; He, Haibo3; Liu, Derong4

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H-infinity control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

KeywordAdaptive Critic Designs Adaptive/approximate Dynamic Programming (Adp) Boundedness Convergence Neural Networks Optimal Control Reinforcement Learning Robust Control Stability
WOS HeadingsScience & Technology ; Technology
WOS KeywordGuaranteed Cost Control ; Optimal-control Design ; Discrete-time-systems ; Infinity State-feedback ; Approximate Optimal-control ; Constrained-input Systems ; Optimal Tracking Control ; Learning Optimal-control ; Horizon Optimal-control ; Load-frequency Control
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(51529701 ; Beijing Natural Science Foundation(4162065) ; U.S. National Science Foundation(ECCS 1053717 ; SKLMCCS ; U1501251 ; CMMI 1526835) ; 61533017 ; 61233001 ; 61520106009)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000409311800039
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Cited Times:125[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA
4.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
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GB/T 7714
Wang, Ding,He, Haibo,Liu, Derong. Adaptive Critic Nonlinear Robust Control: A Survey[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(10):3429-3451.
APA Wang, Ding,He, Haibo,&Liu, Derong.(2017).Adaptive Critic Nonlinear Robust Control: A Survey.IEEE TRANSACTIONS ON CYBERNETICS,47(10),3429-3451.
MLA Wang, Ding,et al."Adaptive Critic Nonlinear Robust Control: A Survey".IEEE TRANSACTIONS ON CYBERNETICS 47.10(2017):3429-3451.
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