改进了基于多细胞超体顶部建模和双层优化智能汽车的强大模型预测轨迹跟踪控制
Xiaoyu Wang1,2, Guowei Dou2, Te Chen2
1School of Mechanical and Electrical Engineering, Suzhou Polytechnic University, Suzhou 215000, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一种改进的可靠模型预测控制 (RMPC) 用于车辆轨迹跟踪. RMPC方法提高了对模型参数不确定性的控制精度和稳定性,提高了安全性.
科学领域:
- 汽车工程 汽车工程
- 控制系统理论 控制系统理论
- 机器人技术 机器人技术 机器人技术
背景情况:
- 车辆轨迹跟踪对于自动驾驶至关重要.
- 模型参数的不确定性对控制系统性能构成重大挑战.
- 传统模型预测控制 (MPC) 可以对这些扰动敏感.
研究的目的:
- 提出一个改进的强大的模型预测控制 (RMPC) 方法.
- 为了解决车辆轨迹跟踪控制中的模型参数扰动问题.
- 提高车辆控制系统的稳定性和实时性能.
主要方法:
- 使用了两度自由度的车辆模型和Serret Frenet错误模型.
- 采用多细胞超立方体顶点建模来表示参数不确定性.
- 实现了双层优化,具有有限时间域优化和终端约束.
- 整合了利亚普诺夫理论来设计一个控制不变集合.
主要成果:
- 峰值侧向偏差从1.0米减少到0.2米.
- 汇聚的航向偏差在2度以内.
- 与传统的MPC相比,平均和根平均平方控制错误显著减少.
- 在参数扰动下表现出良好的稳定性和实时性能.
结论:
- 拟议的RMPC方法有效地处理车辆模型参数扰动.
- 在复杂的道路条件和车辆状态转换中,RMPC提供了卓越的性能.
- 这种方法提高了车辆轨迹跟踪控制的可靠性和安全性.
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