Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints
Yang, Xiong; Liu, Derong; Huang, Yuzhu
发表期刊IET CONTROL THEORY AND APPLICATIONS
2013-11-21
卷号7期号:17页码:2037-2047
文章类型Article
摘要In 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.
关键词Adaptive 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标题词Science & Technology ; Technology
关键词[WOS]SATURATING ACTUATORS ; STABILIZATION ; STABILITY
收录类别SCI
语种英语
WOS研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号WOS:000326109800001
引用统计
被引频次:110[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/3848
专题复杂系统管理与控制国家重点实验室_复杂系统智能机理与平行控制团队
作者单位Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
第一作者单位中国科学院自动化研究所
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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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