模糊神经网络基于PID的恒定减速控制用于使用EMB系统的自动化矿山电动车辆
Jian Li1, Chi Ma1, Yuqiang Jiang1
1School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
自动化电动矿车现在具有恒定减速制动,以实现更安全,更顺的操作. 一个新的模糊神经网络 PID 控制器确保了精确的制动控制,增强了矿山运输自动化.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 控制系统工程 控制系统工程
- 汽车工程 汽车工程
背景情况:
- 目前,地下无轨电动轮车 (UETRV) 使用多级别的恒定制动扭矩控制.
- 该系统缺乏用于目标减速的闭环控制,导致安全和舒适性问题.
- 不准确的停车距离是现有系统的一个重大缺点.
研究的目的:
- 为UETRVs开发和验证一个恒定减速制动系统.
- 为了提高自动化采矿车辆的安全性,舒适性和停止精度.
- 引入一个闭环控制算法,以提高制动性能.
主要方法:
- 描述了带有电机驱动器的恒定减速制动系统的架构和工作原理.
- 在Matlab/Simulink中使用模糊神经网络PID提出并模拟了一个减速闭环控制算法.
- 在工业现场设置中建造并测试了一个实际的车控制单元 (BCU).
主要成果:
- 模糊神经网络 PID 算法在 ±0.1 m/s2 的偏差范围内实现了恒定减速控制.
- 这种性能超过了模糊的PID (±0.11 m/s2) 和经典的PID (±0.13 m/s2).
- 该系统证明了快速实现目标减速 (-2.5 m/s2).
结论:
- 开发的恒定减速控制算法对于UETRV是可行的.
- 新的BCU提供了积极和平稳的减速,提高了车辆的安全性和自动化.
- 这一进步提高了矿山运输机械的电气化和自动化水平.
相关概念视频
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