Reinforcement Learning Based Speed Control with Creep Rate Constraints for Autonomous Driving of Mining Electric Locomotives

被引：1
|
作者：
Li, Ying [1 ]
Zhu, Zhencai [1 ]
Li, Xiaoqiang [2 ]
机构：
[1] China Univ Min & Technol, Sch Mech & Elect Engn, Xuzhou 221116, Peoples R China
[2] China Univ Min & Technol, Sch Elect Engn, Xuzhou 221116, Peoples R China
来源：
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 11期
关键词：
autonomous driving; creep rate; mining electric locomotive; reinforcement learning; speed control; FORCES;
D O I：
10.3390/app14114499
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
The working environment of mining electric locomotives is wet and muddy coal mine roadway. Due to low friction between the wheel and rail and insufficient utilization of creep rate, there may be idling or slipping between the wheels and rails of mining electric locomotives. Therefore, it is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control algorithm for mining electric locomotives based on improved epsilon-greedy is theoretically proven to be convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail under wet and slippery road conditions, it is concluded that the value of creep rate is an important factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous control method for mining electric locomotives based on creep control is proposed in this paper. Finally, the effectiveness of the proposed method is verified through simulation. The problem of wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining environments is effectively suppressed. Autonomous operation of vehicles with optimal driving efficiency can be achieved through quantitative control and utilization of the creep rate between wheels and rails.
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页数：20
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