优化无人驾驶个人移动的方向盘,使用新的差分哈里斯·霍克斯优化算法 (DHHO) 和编码器建模
Mohamed Reda1,2, Ahmed Onsy1, Amira Y Haikal2
1School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
一个新的差分哈里斯霍克斯优化 (DHHO) 算法增强了阿克曼移动性滑板车的方向控制. 这种方法显著提高了自动驾驶系统的响应时间和准确性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 转向性能对于自动驾驶系统 (ADS) 来说至关重要,特别是在定位和路径规划方面.
- 超启发式优化算法已经在指导控制应用程序方面表现出了很大的前景.
- 哈里斯·霍克斯优化 (HHO) 算法是有效的,但尚未应用于转向控制.
研究的目的:
- 为了提高阿克曼个人移动性滑板车的转向性能.
- 介绍一个新的元启发式优化算法,差分哈里斯霍克斯优化 (DHHO).
- 为增强控制而建模方向盘编码器.
主要方法:
- 使用监督学习来建模实际实验中的方向盘编码器传感器数据.
- 通过将突变纳入HHO探索阶段来增强多样性来开发DHHO算法.
- DHHO 实现了实时 PID 调整以控制滑板车转向.
主要成果:
- 在CEC2021基准函数上,DHHO表现优于HHO,PSO,BAS和CMAES.
- 与HHO相比,DHHO在10维和20维问题中实现了显著的错误减少.
- 实践测试显示,滑板车转向的定位时间有89.31%的改善,没有超速或稳定状态错误.
结论:
- DHHO算法为移动性滑板车的方向控制提供了显著的进步.
- DHHO提供了一种强大而高效的方法来优化自动驾驶系统的性能.
- 拟议的DHHO算法优于传统的控制方法,证明了其实际适用性.
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