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Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints
Yang, Xiong; Liu, Derong; Huang, Yuzhu
Source PublicationIET CONTROL THEORY AND APPLICATIONS
2013-11-21
Volume7Issue:17Pages:2037-2047
SubtypeArticle
AbstractIn this study, an online adaptive optimal control scheme is developed for solving the infinite-horizon optimal control problem of uncertain non-linear continuous-time systems with the control policy having saturation constraints. A novel identifier-critic architecture is presented to approximate the Hamilton-Jacobi-Bellman equation using two neural networks (NNs): an identifier NN is used to estimate the uncertain system dynamics and a critic NN is utilised to derive the optimal control instead of typical action-critic dual networks employed in reinforcement learning. Based on the developed architecture, the identifier NN and the critic NN are tuned simultaneously. Meanwhile, unlike initial stabilising control indispensable in policy iteration, there is no special requirement imposed on the initial control. Moreover, by using Lyapunov's direct method, the weights of the identifier NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. Finally, an example is provided to demonstrate the effectiveness of the present approach.
KeywordAdaptive Control Approximation Theory Closed Loop Systems Continuous Time Systems Lyapunov Methods Neurocontrollers Nonlinear Control Systems Optimal Control Robust Control Uncertain Systems Neural Network-based Online Adaptive Optimal Control Uncertain Nonlinear Continuous-time Systems Control Constraints Infinite-horizon Optimal Control Problem Control Policy Saturation Constraints Identifier-critic Architecture Hamilton-jacobi-bellman Equation Approximation Uncertain System Dynamics Critic Nn Action-critic Dual Networks Reinforcement Learning Identifier Nn Policy Iteration Lyapunovaeuros Direct Method Closed Loop System Stability
WOS HeadingsScience & Technology ; Technology
WOS KeywordSATURATING ACTUATORS ; STABILIZATION ; STABILITY
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000326109800001
Citation statistics
Cited Times:64[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3848
Collection复杂系统管理与控制国家重点实验室_智能化团队
AffiliationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang, Xiong,Liu, Derong,Huang, Yuzhu. Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints[J]. IET CONTROL THEORY AND APPLICATIONS,2013,7(17):2037-2047.
APA Yang, Xiong,Liu, Derong,&Huang, Yuzhu.(2013).Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints.IET CONTROL THEORY AND APPLICATIONS,7(17),2037-2047.
MLA Yang, Xiong,et al."Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints".IET CONTROL THEORY AND APPLICATIONS 7.17(2013):2037-2047.
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