Adaptive Q-Learning Based Model-Free $H_{\infty }$ Control of Continuous-Time Nonlinear Systems: Theory and Application

IF 5.3 3区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jun Zhao;Yongfeng Lv;Zhangu Wang;Ziliang Zhao
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引用次数: 0

Abstract

Although model based $H_{\infty }$ control scheme for nonlinear continuous-time (CT) systems with unknown system dynamics has been extensively studied, model-free $H_{\infty }$ control of nonlinear CT systems via Q-learning is still a challenging problem. This paper develops a novel Q-learning based model-free $H_{\infty }$ control scheme for nonlinear CT systems, where the adaptive critic and actor continuously and simultaneously update each other, eliminating the need for iterative steps. As a result, a hybrid structure is avoided and there is no longer a requirement for an initial stabilizing control policy. To obtain the $H_{\infty }$ control of the CT nonlinear system, the Q-learning strategy is introduced to online resolve the $H_{\infty }$ control problem in a non-iterative approach, where the system dynamics are not required. In addition, a new learning law is further developed by utilizing a sliding mode scheme to online update the critic neural network (NN) weights. Due to the strong convergence of critic NN weights, the actor NN used in most $H_{\infty }$ control algorithms is removed. Finally, numerical simulation and experimental results of an adaptive cruise control (ACC) system based on a real vehicle effectively demonstrate the feasibility of the presented control method and learning algorithm.
基于自适应q学习的无模型$H_{\infty }$连续非线性系统控制:理论与应用
尽管基于模型的非线性连续时间(CT)系统的$H_{\infty }$控制方案已经得到了广泛的研究,但基于q -学习的非线性连续时间(CT)系统的无模型$H_{\infty }$控制仍然是一个具有挑战性的问题。本文提出了一种新的基于q学习的非线性CT系统无模型$H_{\infty }$控制方案,其中自适应评论家和行动者连续且同步地相互更新,消除了迭代步骤的需要。因此,避免了混合结构,不再需要初始稳定控制策略。为了获得CT非线性系统的$H_{\infty }$控制,引入q -学习策略,在不需要系统动力学的情况下,采用非迭代方法在线求解$H_{\infty }$控制问题。此外,利用滑模方法在线更新评价神经网络(NN)权值,进一步发展了一种新的学习规律。由于批判神经网络权值的强收敛性,大多数$H_{\infty }$控制算法中使用的行动者神经网络被去除。最后,基于真实车辆的自适应巡航控制(ACC)系统的数值仿真和实验结果有效地验证了所提出的控制方法和学习算法的可行性。
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来源期刊
CiteScore
10.30
自引率
7.50%
发文量
147
期刊介绍: The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.
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