通过集成剩余动力学学习来实现自动驾驶车辆路径跟踪的学习增强的MPC.
Lu Xiong1, Ming Liu1, Zhihao Xie1
1School of Automotive Studies, Tongji University, 4800 Cao'an Highway, Jiading District, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了一种学习增强型模型预测控制 (MPC) 框架与数据驱动动力学改进 (DDR) 模型. 它在具有挑战性的驾驶条件下显著提高了车辆路径跟踪精度和稳定性.
科学领域:
- 控制工程 控制工程 控制工程
- 机器学习 机器学习
- 汽车动力学 汽车动力学
背景情况:
- 准确的车辆动态建模对于路径跟踪控制至关重要.
- 非线性和时间变化的行为降低了传统的模型预测控制 (MPC) 性能.
研究的目的:
- 提出一个学习增强的MPC框架,使用数据驱动动力学改进 (DDR) 模型.
- 为了提高车辆路径跟踪的预测准确性和控制稳定性.
主要方法:
- 一个基于整体学习的DDR模型补充了车辆名义动态.
- 一组神经预测器可以提高概括性和稳定性.
- 一个特征驱动的激活机制选择性地应用精细化来减少计算负载.
主要成果:
- 精细的动态显著提高了跟踪准确性和稳定性.
- 最大横向偏差减少了~6厘米 (单车道变化) 和~4厘米 (双车道变化).
- 最大的航向误差分别减少了0.02 rad和0.015 rad.
结论:
- 拟议的学习增强的MPC框架有效地增强了车辆路径跟踪.
- DDR模型准确地捕捉复杂的动态,在苛刻的条件下提高控制性能.
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