基于强化学习的切换系统综合性能优化设计  

Reinforce learning based design for overall performance optimization of switched linear systems

作　　者：孙振东 王苗苗 SUN Zhendong;WANG Miaomiao(College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China;Key Lab of Systems&Control,Academy of Mathematics&Systems Science,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]山东科技大学电气与自动化工程学院,山东青岛266590 [2]中国科学院数学与系统科学研究院系统控制重点实验室,北京100190

出　　处：《厦门大学学报（自然科学版）》2022年第6期961-966,共6页Journal of Xiamen University：Natural Science

基　　金：国家重点研发项目(2018YFA0703800);山东省自然科学重大基础研究项目(ZR2020ZD26)。

摘　　要：研究连续时间切换自治线性系统综合性能优化,基于分阶段设计思路计算其最小(状态模)超调、在超调约束下的最小调节时间、在过渡过程约束下的最快指数收敛速度.借鉴强化学习方法,通过以模压缩路径的策略空间进行扩展获得有限步结束的迭代优化算法.数值仿真验证了所提算法的有效性.This work addresses the optimization of overall performance for switched linear autonomous systems,and computes piece by piece the minimum(state norm)overshoot,the minimum settling time under the overshoot constraint,and the largest exponential convergence rate under the transient performance constraints.Utilizing the reinforce-learning approach,we obtain finite-time-stopping iterations based on extending the police space.A numerical example is presented to validate the effectiveness of these proposed algorithms.

关 键 词：切换线性系统 渐近性能 超调量 调节时间 强化学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]