A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations
Behzad Farzanegan1
Mohsen Zamani2
Amir Abolfazl Suratgar3
Mohammad Bagher Menhaj1
1.Computational Intelligence Lab,Department of Electrical Engineering,Amirkabir University of Technology,Tehran,Iran2.The School of Electrical Engineering and Computer Science,The University of Newcastle,Newcastle,Australia;Department of Medical Physics and Engineering,Shiraz University of Medical Sciences,Shiraz,Iran3.Distributed Intelligent Optimization Research Lab,Department of Electrical Engineering,Amirkabir University of Technology,Tehran,Iran
摘要：In this study,an adaptive neuro-observer-based optimal control (ANOPC) policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton-Jacobi-Bellman (HJB) framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and alge-braic parts.In the analytical part,first,an observer-based neural network (NN) approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness (UUB) of all signals of the whole closed-loop system is ensured through Lyapunov's direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.
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论文发表日期：2021-05-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：12( 283-294 )
英文信息