基于模型预测控制的低速度无人驾驶车辆的准确轨迹跟踪方法
Lifen Wang1, Sizhong Chen1, Hongbin Ren2
1Beijing Institute of Technology, Beijing, 100081, People's Republic of China.
Scientific reports
|May 10, 2024
概括
本研究引入了一种新的轨迹跟踪方法,用于适应道路曲率的低速车辆. 通过在线性和非线性模型预测控制 (MPC) 之间智能切换,它显著提高了复杂道路上的跟踪精度.
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
- 应用数学 应用数学 应用数学
背景情况:
- 传统的车辆轨迹跟踪模型预测控制 (MPC) 经常假设简单的道路地形,导致现实世界的场景的精度降低.
- 实际的道路状况经常涉及不同的曲率,目前的简化模型无法充分解决这些问题.
- 低速车辆的跟踪精度低,可能会损害安全和运营效率.
研究的目的:
- 为低速车辆开发一种先进的轨迹跟踪方法,以考虑道路曲率.
- 提高模型预测控制 (MPC) 在动态道路环境中的适应性和准确性.
- 为了在具有复杂和时间变化的曲率的道路上实现强大的轨迹跟踪.
主要方法:
- 提出了一种新的轨迹跟踪方法,利用模型预测控制 (MPC),根据道路曲率在控制类型之间自动切换.
- 实施线性模型预测控制 (LMPC) 对于道路曲率小的段落.
- 采用非线性模型预测控制 (NMPC),结合道路曲率效应,用于道路曲率较大的段落和时间变化的条件.
主要成果:
- 与基本的MPC模型相比,模拟比较表明,在各种道路类型和速度上,轨迹跟踪精度显著提高.
- 拟议的方法有效地处理有小和大曲率的道路,包括时间变化的曲率.
- 保持了实时计算效率,确保了实际应用.
结论:
- 开发的轨迹跟踪方法,基于道路曲率在LMPC和NMPC之间进行智能切换,提高了复杂道路的准确性.
- 这种自适应控制策略适合在任意复杂的道路几何形状上跟踪轨迹.
- 这些发现为在各种驾驶条件下更可靠的自主导航和控制系统铺平了道路.
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