文章基本信息

标题：Deep Reinforcement Learning based Super Twisting Controller for Liquid Slosh Control Problem
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作者：Ashish Kumar Shakya ; Kshitij Bithel ; G.N. Pillai 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：1
页码：734-739
DOI：10.1016/j.ifacol.2022.04.120
语种：English
出版社：Elsevier
摘要：AbstractDeep Reinforcement Learning (DRL) based parameter optimization of super twisting control (STC) for the liquid slosh control problem in a moving vehicle is proposed in this paper. The slosh control problem, including the vehicle dynamics, represents an under-actuated nonlinear dynamical system. The slosh phenomenon is modeled by a simple pendulum on a cart and STC had been designed in the literature for the system when the vehicle motion is in a straight line. In this paper, a DRL framework is designed for the first time to tune the STC parameters in order to deliver near optimal performance. The effectiveness of this proposed learning-based STC for the slosh control problem is validated in a Python simulation environment and its performance compared to that of the existing STC design without the learning.
关键词：KeywordsDeep reinforcement learningsuper twisting controllateral sloshunder-actuated system