Data-based neural controls for an unknown continuous-time multi-input system with integral reinforcement  

作　　者：Yongfeng Lv Jun Zhao Wan Zhang Huimin Chang 

机构地区：[1]College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan,Shanxi,030024,China [2]College of Transportation,Shandong University of Science and Technology,Qingdao,266590,Shandong,China [3]School of Mathematical Sciences,Shanxi University,Taiyuan,030006,Shanxi,China

出　　处：《Control Theory and Technology》2025年第1期118-130,共13页控制理论与技术(英文版)

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.

关 键 词：Adaptive dynamic programming Integral reinforcement Neural networks Heuristic dynamic programming Multi-input system 

分 类 号：O17[理学—数学]