Continuous-time receding-horizon reinforcement learning and its application to path-tracking control of autonomous ground vehicles

Lu Yang; Li Wenzhang; Zhang XinglongXu Xin

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Continuous-time receding-horizon reinforcement learning and its application to path-tracking control of autonomous ground vehicles

机译：Continuous-time receding-horizon reinforcement learning and its application to path-tracking control of autonomous ground vehicles

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Reinforcement learning (RL) and approximate dynamic programming (ADP) have been recently studied to solve nonlinear optimal control problems (OCPs) of continuous-time (CT) systems. However, online learning efficiency and reliability are two major concerns to be further improved. Motivated by the above issues, in this paper we propose a receding-horizon reinforcement learning (RHRL) algorithm for near-optimal control of CT systems under control constraints. Different from classic RL and ADP, in the proposed approach, the infinite-horizon OCP is decomposed as a series of finite-horizon ones solved with an actor-critic structure according to the receding horizon strategy, which can improve the online learning efficiency and reliability. The unknown dynamics of the system are identified offline using a sparse kernel-based neural network structure whose weights are also updated online in the RHRL framework to improve the control performance. Moreover, the convergence of the modeling error is proven. To verify the effectiveness of our approach, we apply the RHRL algorithm to the autonomous ground vehicle for realizing near-optimal path-tracking control. Compared with CT model predictive control using a nominal model and other model-free tracking controllers such as pure pursuit, heuristic dual programming, and the soft actor-critic algorithm, RHRL performs better in terms of control performance.

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来源
《Optimal Control Applications and Methods》 |2023年第3期|1129-1147|共19页
作者
Lu Yang; Li Wenzhang; Zhang XinglongXu Xin;
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作者单位

Natl Univ Def Technol;

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原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
autonomous ground vehicles; continuous-time system; optimal control problems; receding horizon; reinforcement learning; unknown dynamics; MODEL-PREDICTIVE CONTROL; SYSTEMS; ROBOT; ITERATION;

Continuous-time receding-horizon reinforcement learning and its application to path-tracking control of autonomous ground vehicles

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