在潮湿和滑坡道路上高速车辆的轨迹跟踪控制
Xiaohua Song1, Kuifeng Chen1, Yujia Zheng1
1School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了一种用于自动驾驶汽车的新型模型预测控制 (MPC) 融合PID控制算法. 它显著提高了轨迹跟踪精度和车辆在滑动道路上的安全性.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 控制理论 控制理论
- 车辆动力学 车辆动力学
背景情况:
- 模型预测控制 (MPC) 广泛用于自动驾驶汽车轨迹跟踪.
- 非线性模型预测控制 (NMPC) 由于系统复杂性而面临准确性和计算负载的挑战.
- 在潮湿,滑动的道路上高速驾驶对自动驾驶汽车提出了重大控制挑战.
研究的目的:
- 开发一种新的控制算法,将MPC与比例积分导数 (PID) 控制融合在一起.
- 为了提高在不利的驾驶条件下自动驾驶汽车的轨迹跟踪精度.
- 为了优化车辆的舒适性,经济性和安全性,同时确保实时控制.
主要方法:
- 提出了一种混合控制算法,该算法结合了模型预测控制 (MPC) 和比例积分导数 (PID) 控制.
- 在Carsim/MATLAB Simulink中利用联合模拟进行性能评估.
- 在高速驾驶条件下,在湿滑的路面上测试了算法.
主要成果:
- 在湿滑的道路上实现了至少22.2%的轨迹跟踪精度的提高.
- 车辆舒适度提高至少9.4%.
- 车辆经济性至少提高了19.8%,安全性至少提高了5.3%.
结论:
- 与MPC融合的PID控制算法有效地提高了自动驾驶汽车的轨迹跟踪精度.
- 提出的方法成功优化了车辆的舒适性,经济性和安全性指数.
- 在高速驾驶场景中,在具有挑战性的道路表面上表现出卓越的性能.
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