适应型预测控制模型用于半自动驾驶汽车的强大的横向运动跟踪,具有动态参数变化
Kumlachew Yeneneh1, Bisrat Yoseph2, Gadisa Sufe3
1Department of Motor Vehicle Engineering, College of Engineering, Ethiopian Defence University, P.O. Box 1041, Bishoftu, Ethiopia. kumynnh2023@gmail.com.
Scientific reports
|November 29, 2025
概括
本研究引入了半自动驾驶汽车的自适应模型预测控制 (AMPC),在不确定的条件下显著减少横向跟踪误差. 新的框架在先进的驾驶辅助系统中提高了车辆的稳定性和安全性.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术和自主系统
- 车辆动力学 车辆动力学
背景情况:
- 由于非线性动力学和外部干扰,传统控制器在保持车辆稳定性和准确性方面面临挑战.
- 半自动驾驶汽车需要强大的控制系统,能够适应车辆参数和环境条件的实时变化.
研究的目的:
- 开发和评估一种新的自适应模型预测控制 (AMPC) 框架,用于在半自动驾驶汽车中强大的横向运动跟踪.
- 提高在动态不确定条件下运行的车辆控制系统的适应性和稳定性.
主要方法:
- 集成实时参数估计使用递归最小平方与预测优化结构.
- 在 MATLAB/Simulink 中开发一个全面的模拟环境,用于严格的测试.
- 在各种场景中进行评估,包括积极的车道更改,横风干扰和低摩擦条件 (μ=0.4).
主要成果:
- 与传统的MPC和LQR相比,AMPC显示横向跟踪误差减少了43%,曲率RMSE改善了37%.
- 即使在严重的干扰下,峰值曲率误差也保持在0.275半径以下,方向盘控制信号流.
- 人类在循环中的模拟证实了稳定性和轨迹跟踪,驾驶员的干预延迟 (1-3秒).
结论:
- 拟议的AMPC框架为半自动驾驶汽车中复杂的横向控制任务提供了卓越的稳定性和适应性.
- 这种可扩展的解决方案提高了共享控制驾驶环境和高级驾驶辅助系统 (ADAS) 的安全性和可靠性.
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