CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics
Zhu, Yuanheng1; Zhao, Dongbin1; Li, Xiangjun2
Source PublicationIET CONTROL THEORY AND APPLICATIONS
2016-08-08
Volume10Issue:12Pages:1339-1347
SubtypeArticle
AbstractThe optimal tracking of non-linear systems without knowing system dynamics is an important and intractable problem. Based on the framework of reinforcement learning (RL) and adaptive dynamic programming, a model-free adaptive optimal tracking algorithm is proposed in this study. After constructing an augmented system with the tracking errors and the reference states, the tracking problem is converted to a regulation problem with respect to the new system. Several RL techniques are synthesised to form a novel algorithm which learns the optimal solution online in real time without any information of the system dynamics. Continuous adaptation laws are defined by the current observations and the past experience. The convergence is guaranteed by Lyapunov analysis. Two simulations on a linear and a non-linear systems demonstrate the performance of the proposed approach.
KeywordNonlinear Control Systems Continuous Time Systems Learning (Artificial Intelligence) Optimal Control Dynamic Programming Lyapunov Methods Linear Systems Reinforcement Learning Continuous-time Problem Nonlinear Optimal Tracking Problem Adaptive Dynamic Programming Model-free Adaptive Optimal Tracking Algorithm Lyapunov Analysis Linear System
WOS HeadingsScience & Technology ; Technology
DOI10.1049/iet-cta.2015.0769
WOS KeywordADAPTIVE OPTIMAL-CONTROL ; UNKNOWN DYNAMICS ; POLICY ITERATION ; LINEAR-SYSTEMS ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(61273136 ; Beiing Nova Program(Z141101001814094) ; Science and Technology Foundation of SGCC(DG71-14-032) ; 61573353 ; 61533017)
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000381410000004
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12655
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.China Elect Power Res Inst, Elect Engn & New Mat Dept, Beijing 100192, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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Zhu, Yuanheng,Zhao, Dongbin,Li, Xiangjun. Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics[J]. IET CONTROL THEORY AND APPLICATIONS,2016,10(12):1339-1347.
APA Zhu, Yuanheng,Zhao, Dongbin,&Li, Xiangjun.(2016).Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics.IET CONTROL THEORY AND APPLICATIONS,10(12),1339-1347.
MLA Zhu, Yuanheng,et al."Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics".IET CONTROL THEORY AND APPLICATIONS 10.12(2016):1339-1347.
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