Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal Control  

作　　者：Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang 

机构地区：[1]IEEE [2]the Department of Industrial Engineering,Tsinghua University,Beijing 100084,China [3]The State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第7期1702-1704,共3页自动化学报（英文版）

基　　金：supported by the Motion G,Inc.Collaborative Research Project for Fundamental Modeling and Parallel Drive-Control of Servo Drive Systems。

摘　　要：Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关 键 词：DEEP LETTER enable 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP13[自动化与计算机技术—控制科学与工程]