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自动驾驶汽车的快速轨迹跟踪控制算法基于交替方向乘法 (ADMM) 来实现模型预测控制 (MPC) 的倒退优化
Ding Dong1, Hongtao Ye1,2,3, Wenguang Luo1,3
1School of Automation, Guangxi University of Science and Technology, Liuzhou 545036, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究提高了自动驾驶汽车的轨迹跟踪使用交替方向乘法 (ADMM) 与模型预测控制 (MPC). ADMM方法显著提高了计算速度和跟踪精度,以实现实时性能.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 汽车工程 汽车工程
- 计算优化计算优化
背景情况:
- 实时轨迹跟踪对于自动驾驶汽车的安全性和效率至关重要.
- 模型预测控制 (MPC) 是一种常见的控制策略,但其计算需求可以限制实时性能.
- 现有的方法,如主动集法 (ASM) 和内部点法 (IPM),面临着计算速度的挑战.
研究的目的:
- 为了提高自动驾驶汽车轨迹跟踪的实时性能.
- 为了提高模型预测控制 (MPC) 算法的计算速度.
- 评估交替方向乘法 (ADMM) 在优化MPC对车辆控制的有效性.
主要方法:
- 在模型预测控制 (MPC) 的回退优化中应用了交替方向乘法 (ADMM).
- 制定了车辆动力学模型和输出方程,引入辅助和双变量.
- 将二次编程问题和车辆动态约束转化为ADMM可解决的形式,使用下降的惩罚因子.
主要成果:
- 拟议的基于ADMM的MPC算法证明了轨迹跟踪精度的提高.
- 与主动集法 (ASM) 和内部点法 (IPM) 相比,该方法实现了优越的实时性能.
- 即使增加了预测时间域,计算时间也保持稳定,证实了ADMM的有效性.
结论:
- ADMM方法有效地提高了MPC用于自动驾驶汽车轨迹跟踪的实时计算速度.
- 提出的方法为实现自动驾驶系统高精度和实时性能提供了可行的解决方案.
- 集成ADMM在优化动态车辆应用的控制算法方面取得了重大进展.
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