Hierarchical speed control for autonomous electric vehicle through deep reinforcement learning and robust control
| Autor: | Guangfei Xu, Peisong Diao, Xiangkun He, Chen Meizhou, Huanxiao Pang, Jian Wu, Miao Hequan, Wenjun Wang |
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| Rok vydání: | 2021 |
| Předmět: |
Electronic speed control
Control and Optimization business.product_category Control engineering systems. Automatic machinery (General) Computer science Computer Science Applications Human-Computer Interaction Control and Systems Engineering Control theory TJ212-225 Electric vehicle Reinforcement learning Electrical and Electronic Engineering Robust control business |
| Zdroj: | IET Control Theory & Applications, Vol 16, Iss 1, Pp 112-124 (2022) |
| ISSN: | 1751-8652 1751-8644 |
| DOI: | 10.1049/cth2.12211 |
| Popis: | For the speed control system of autonomous electric vehicle (AEV), challenge happens with how to determine an appropriate driving speed to satisfy the dynamic environment while resisting uncertainty and disturbance. Therefore, this paper proposes a robust optimal speed control approach based on hierarchical architecture for AEV through combining deep reinforcement learning (DRL) and robust control. In decision‐making layer, a deep maximum entropy proximal policy optimization (DMEPPO) algorithm is presented to obtain an optimal speed via dynamic environment information, heuristic target entropy and adaptive entropy constraint. In motion control layer, to track the learned optimal speed while resisting uncertainty and disturbance, a robust speed controller is designed by the linear matrix inequality (LMI). Finally, simulation experiment results show that the proposed robust optimal speed control scheme based on hierarchical architecture for AEV is feasible and effective. |
| Databáze: | OpenAIRE |
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