Reinforcement Learning Behavioral Control for Nonlinear Autonomous System  

作　　者：Zhenyi Zhang Zhibin Mo Yutao Chen Jie Huang 

机构地区：[1]the College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108 [2]the Key Laboratory of Industrial Automation Control Technology and Information Processing,Education Department of Fujian Province,Fuzhou 350108 [3]G+Industrial Internet Institute,Fuzhou University,Fuzhou 350108,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第9期1561-1573,共13页自动化学报（英文版）

基　　金：the National Natural Science Foundation of China(61603094)。

摘　　要：Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers.In this work,a novel twolayer reinforcement learning behavioral control(RLBC)method is proposed to reduce such dependence by trial-and-error learning.Specifically,in the upper layer,a reinforcement learning mission supervisor(RLMS)is designed to learn the optimal mission priority.Compared with existing mission supervisors,the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural networks.In the lower layer,a reinforcement learning controller(RLC)is designed to learn the optimal control policy.Compared with existing behavioral controllers,the RLC reduces the control cost of mission priority adjustment by balancing control performance and consumption.All error signals are proved to be semi-globally uniformly ultimately bounded(SGUUB).Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers,respectively.

关 键 词：Behavioral control mission supervisor nonlinear autonomous system reinforcement learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]