Data-Driven Direct Adaptive Risk-Sensitive Control of Stochastic Systems

作     者：QIAO Nan LI Tao QIAO Nan;LI Tao

作者机构：School of Mathematical SciencesEast China Normal UniversityShanghai 200241China Shanghai Key Laboratory of Pure Mathematics and Mathematical PracticeSchool of Mathematical SciencesEast China Normal UniversityShanghai 200241China 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2024年第37卷第4期

页      面：1446-1469页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0701[理学-数学] 

基　　金：supported in part by the National Natural Science Foundation of China under Grant No.62261136550 in part by the Basic Research Project of Shanghai Science and Technology Commission under Grant No.20JC1414000 

主　　题：Adaptive dynamic programming direct adaptive control generalized algebraic Riccati equation risk-sensitive control 

摘      要：The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming(ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive *** authors use online data of the system to iteratively solve the generalized algebraic Riccati equation(GARE) and to learn the optimal control law *** the case with measurable system noises,the authors show that the adaptive control law approximates the optimal control law as time goes *** the case with unmeasurable system noises,the authors use the least-square solution calculated only from the measurable data instead of the real solution of the regression equation to iteratively solve the *** authors also study the influences of the intensity of the system noises,the intensity of the exploration noises,the initial iterative matrix,and the sampling period on the convergence of the ADP ***,the authors present two numerical simulation examples to demonstrate the effectiveness of the proposed algorithms.