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Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems
Yang, Xiong1,2; He, Haibo2; Liu, Derong3; Zhu, Yuanheng4
AbstractThe design of robust controllers for continuous-time (CT) non-linear systems with completely unknown non-linearities is a challenging task. The inability to accurately identify the non-linearities online or offline motivates the design of robust controllers using adaptive dynamic programming (ADP). In this study, an ADP-based robust neural control scheme is developed for a class of unknown CT non-linear systems. To begin with, the robust non-linear control problem is converted into a non-linear optimal control problem via constructing a value function for the nominal system. Then an ADP algorithm is developed to solve the non-linear optimal control problem. The ADP algorithm employs actor-critic dual networks to approximate the control policy and the value function, respectively. Based on this architecture, only system data is necessary to update simultaneously the actor neural network (NN) weights and the critic NN weights. Meanwhile, the persistence of excitation assumption is no longer required by using the Monte Carlo integration method. The closed-loop system with unknown non-linearities is demonstrated to be asymptotically stable under the obtained optimal control. Finally, two examples are provided to validate the developed method.
KeywordDynamic Programming Robust Control Neurocontrollers Continuous Time Systems Control System Synthesis Nonlinear Control Systems Optimal Control Function Approximation Monte Carlo Methods Closed Loop Systems Asymptotic Stability Adaptive Dynamic Programming Robust Neural Control Design Unknown Continuous-time Nonlinear Systems Ct Nonlinear Systems Adp-based Robust Neural Control Scheme Robust Nonlinear Control Problem Nonlinear Optimal Control Problem Nominal System Adp Algorithm Actor-critic Dual Networks Control Policy Approximation Value Function Approximation Actor Neural Network Weights Critic Nn Weights Monte Carlo Integration Method Closed-loop System Asymptotically Stability
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61503379 ; U.S. National Science Foundation(ECCS 1053717 ; 61533017 ; CMMI 1526835) ; 61603382 ; 51529701)
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000409425700015
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Cited Times:17[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
2.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA
3.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang, Xiong,He, Haibo,Liu, Derong,et al. Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems[J]. IET CONTROL THEORY AND APPLICATIONS,2017,11(14):2307-2316.
APA Yang, Xiong,He, Haibo,Liu, Derong,&Zhu, Yuanheng.(2017).Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems.IET CONTROL THEORY AND APPLICATIONS,11(14),2307-2316.
MLA Yang, Xiong,et al."Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems".IET CONTROL THEORY AND APPLICATIONS 11.14(2017):2307-2316.
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