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ACP-Incorporated Perturbation-Resistant Neural Dynamics Controller for Autonomous Vehicles
Liufu, Ying1; Jin, Long1; Shang, Mingsheng2; Wang, Xingxia3,4; Wang, Fei-Yue3,5
2024-04-01
摘要Autonomous vehicle control systems are unavoidably influenced by diverse noise perturbations from the unpredictable external environment and internal system. In this consideration, based on the model predictive control (MPC) strategy, a perturbation-resistant neural dynamics (PRND) controller equipped with the noise-suppression ability for the path-tracking control of autonomous vehicles is newly designed in this paper, under the framework of artificial systems, computational experiments, and parallel execution (ACP). In addition, theoretical analyses show that the proposed ACP-incorporated PRND controller can behave with exponential convergence and strong robustness under different noise scenarios. Lastly, computational experiments are conducted and parallelly executed on the CarSim-Simulink platform and E-Car physical platform to demonstrate the effectiveness and superiority of the proposed controller. Overall, this paper provides a new perspective for designing neural-dynamics-based controllers for autonomous vehicles, thereby guaranteeing reliable control performance and effectively resisting noise perturbations.
关键词Autonomous vehicles Vehicle dynamics Kinematics Task analysis Control systems Robustness Predictive control Model predictive control (MPC) neural dynamics autonomous vehicles artificial systems computational experiments and parallel execution (ACP)
DOI10.1109/TIV.2023.3348632
发表期刊IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN2379-8858
卷号9期号:4页码:4675-4686
通讯作者Jin, Long(jinlong@lzu.edu.cn) ; Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
收录类别SCI
WOS记录号WOS:001250038700015
语种英语