基于前+预测性LQR算法与GA优化和PID补偿的智能车辆横向控制战略研究
Zhu-An Zheng1, Zimo Ye2, Xiangyu Zheng1
1School of Automotive Engineering, Yancheng Institute of Technology, No. 1 Middle Hope Avenue, Tinghu District, Yancheng City, 224000, Jiangsu Province, China.
Scientific reports
|September 27, 2024
概括
本研究介绍了针对智能汽车自动驾驶系统的优化LQR算法,增强横向运动控制. 该方法显著提高了在机动期间的跟踪精度和车辆稳定性.
科学领域:
- 汽车工程 汽车工程
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
背景情况:
- 智能汽车自动驾驶系统需要精确的横向运动控制,以确保安全性和性能.
- 现有的控制算法经常面临系统滞后和适应不同条件的挑战.
研究的目的:
- 为智能汽车自动驾驶系统开发先进的横向运动控制算法.
- 通过使用新的控制策略,提高追踪精度和车辆稳定性.
主要方法:
- 设计了一个前+预测的线性正方体调节器 (LQR) 算法,将遗传算法 (GA) 纳入参数优化中.
- 一个比例整数导数 (PID) 转向角度补偿控制器被开发来纠正横向错误.
- 拟议的控制器使用Carsim-Simulink共模拟对双车道变化 (DLC) 和循环条件测试 (CCT) 进行了验证.
主要成果:
- 与传统的LQR控制器相比,优化的LQR控制器在横向和方向错误控制方面取得了超过50%的改进.
- 车辆的侧滑角度和曲率保持在严格的稳定性约束范围内 (-0.05°至0.05°和-0.15rad/s至0.10rad/s,分别).
- 实现了更高的跟踪精度和遵守车辆稳定性约束.
结论:
- 拟议的前+预测性LQR算法,通过GA和PID补偿进行优化,为智能汽车提供了卓越的横向运动控制.
- 这种方法有效地解决了系统滞后,提高了适应性,从而提高了车辆的稳定性和跟踪性能.
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