Optimal Tracking Control for Autonomous Vehicle With Prescribed Performance via Adaptive Dynamic Programming

动态规划 车辆动力学 计算机科学 控制理论(社会学) 最优控制 控制工程 跟踪(教育) 自适应控制 控制(管理) 工程类 数学优化 人工智能 汽车工程 数学 心理学 教育学 算法
作者
Chuan Hu,Ziao Wang,Xiangwei Bu,Jun Zhao,Jing Na,Hongbo Gao
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:: 1-13 被引量:1
标识
DOI:10.1109/tits.2024.3384113
摘要

The path tracking control problem for autonomous vehicle with uncertain dynamics requires simultaneous consideration of control optimality and safety-based performance constraints. In this paper, an adaptive optimal control method with prescribed performance is proposed to solve this problem, which contains two contributions: 1) by introducing a prescribed performance function (PPF) into adaptive dynamic programming (ADP), the controller can constrain the tracking error of the system within a specified performance boundary while optimizing the control cost; 2) the critic-only ADP is used for the controller design, which simplifies the commonly used actor-critic ADP scheme, and the convergence of the estimation error is guaranteed under FE conditions. On this basis, the neural network identification technique is introduced to deal with the unknown dynamic parameters of the vehicle system. The control scheme is able to strictly guarantee user-defined vehicle performance specifications with approximately optimal control performance. The stability of the closed-loop system is rigorously demonstrated by the Lyapunov method. In addition, the controller also embeds a radial basis function neural network (RBFNN) compensator to approximate the nonlinear external disturbances of the autonomous vehicle. Finally, the efficiency of the controller to achieve autonomous vehicle path tracking is verified by CarSim-Simulink simulation.
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