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Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances
Song, Ruizhuo1; Lewis, Frank L.2,3; Wei, Qinglai4; Zhang, Huaguang5
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
2016-05-01
Volume46Issue:5Pages:1041-1050
SubtypeArticle
AbstractAn optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.
KeywordAdaptive Critic Designs Adaptive/approximate Dynamic Programming (Adp) Dynamic Programming Off-policy Optimal Control Unknown System
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCYB.2015.2421338
WOS KeywordTIME NONLINEAR-SYSTEMS ; OPTIMAL TRACKING CONTROL ; ADAPTIVE OPTIMAL-CONTROL ; OPTIMAL-CONTROL SCHEME ; CONTROL DESIGN ; FEEDBACK-CONTROL ; LINEAR-SYSTEMS ; OUTPUT DATA ; ALGORITHM ; ITERATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61304079 ; Beijing Natural Science Foundation(4132078 ; China Post-Doctoral Science Foundation(2013M530527) ; Fundamental Research Funds for the Central Universities(FRF-TP-14-119A2) ; Open Research Project from The State Key Laboratory of Management and Control for Complex Systems(20120106 ; NSF(ECCS-1128050) ; Office of Naval Research(N00014-13-1-0562) ; Air Force Office of Scientific Research through the European Office of Aerospace Research and Development(13-3055) ; U.S. Army Research Office(W911NF-11-D-0001) ; China National Natural Science Foundation of China(61120106011) ; China Education Ministry Project 111(B08015) ; 61374105 ; 4143065) ; 20150104) ; 61433004)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000374989300001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12209
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
2.Univ Texas Arlington, UTA Res Inst, Ft Worth, TX 76118 USA
3.Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
5.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China
Recommended Citation
GB/T 7714
Song, Ruizhuo,Lewis, Frank L.,Wei, Qinglai,et al. Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(5):1041-1050.
APA Song, Ruizhuo,Lewis, Frank L.,Wei, Qinglai,&Zhang, Huaguang.(2016).Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.IEEE TRANSACTIONS ON CYBERNETICS,46(5),1041-1050.
MLA Song, Ruizhuo,et al."Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances".IEEE TRANSACTIONS ON CYBERNETICS 46.5(2016):1041-1050.
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