A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

作     者：Behzad Farzanegan Mohsen Zamani Amir Abolfazl Suratgar Mohammad Bagher Menhaj Behzad Farzanegan;Mohsen Zamani;Amir Abolfazl Suratgar;Mohammad Bagher Menhaj

作者机构：Distributed Intelligent Optimization Research LabDepartment of Electrical EngineeringAmirkabir University of TechnologyTehranIran Computational Intelligence LabDepartment of Electrical EngineeringAmirkabir University of TechnologyTehranIran The School of Electrical Engineering and Computer ScienceThe University of NewcastleNewcastleAustralia Department of Medical Physics and EngineeringShiraz University of Medical SciencesShirazIran 

出 版 物：《Control Theory and Technology》 (控制理论与技术（英文版）)

年 卷 期：2021年第19卷第2期

页      面：283-294页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Input constraints Optimal control Neural networks Nonaffine nonlinear systems Reinforcement learning Unknown dynamics 

摘      要：In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input ***–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control *** consists of both analytical and algebraic *** the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB *** the algebraic part,the optimal control input that does not exceed the saturation bounds is *** weights of two NNs associated with observer and controller are simultaneously updated in an online *** ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct ***,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.