Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications  

作　　者：Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis 

机构地区：[1]IEEE [2]the Faculty of Information Technology,Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing Laboratory of Smart Environmental Protection,and Beijing Institute of Artificial Intelligence,Beijing University of Technology,Beijing 100124,China [3]the School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen 518055,China [4]the Department of Electrical and Computer Engineering,University of Illinois at Chicago,Chicago IL 60607 USA [5]the School of Information and Control Engineering,Liaoning Petrochemical University,Fushun 113001,China [6]the UTA Research Institute,the University of Texas at Arlington,Arlington TX 76118 USA

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第1期18-36,共19页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(62222301, 62073085, 62073158, 61890930-5, 62021003);the National Key Research and Development Program of China (2021ZD0112302, 2021ZD0112301, 2018YFC1900800-5);Beijing Natural Science Foundation (JQ19013)。

摘　　要：Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关 键 词：Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL) 

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]