Data-based neural controls for an unknown continuous-time multi-input system with integral reinforcement
Yongfeng Lv1
Jun Zhao2
Wan Zhang1
Huimin Chang3
1.College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan,Shanxi 030024,China2.College of Transportation,Shandong University of Science and Technology,Qingdao 266590,Shandong,China3.School of Mathematical Sciences,Shanxi University,Taiyuan 030006,Shanxi,China
摘要：Integral reinforcement learning(IRL)is an effective tool for solving optimal control problems of nonlinear systems,and it has been widely utilized in optimal controller design for solving discrete-time nonlinearity.However,solving the Hamilton-Jacobi-Bellman(HJB)equations for nonlinear systems requires precise and complicated dynamics.Moreover,the research and application of IRL in continuous-time(CT)systems must be further improved.To develop the IRL of a CT nonlinear system,a data-based adaptive neural dynamic programming(ANDP)method is proposed to investigate the optimal control problem of uncertain CT multi-input systems such that the knowledge of the dynamics in the HJB equation is unnecessary.First,the multi-input model is approximated using a neural network(NN),which can be utilized to design an integral reinforcement signal.Subsequently,two criterion networks and one action network are constructed based on the integral reinforcement signal.A nonzero-sum Nash equilibrium can be reached by learning the optimal strategies of the multi-input model.In this scheme,the NN weights are constantly updated using an adaptive algorithm.The weight convergence and the system stability are analyzed in detail.The optimal control problem of a multi-input nonlinear CT system is effectively solved using the ANDP scheme,and the results are verified by a simulation study.
机标关键词：systemwithcontinuous-timecontrolsdata-basedintegralmulti-inputneural
论文发表日期：2025-02-04
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：13( 118-130 )
英文信息