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模型预测控制器方法用于自动化车辆的路径跟踪
1Department of Automotive Technologies, Budapest University of Technology and Economics, 1111 Budapest, Hungary.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种用于自动驾驶汽车的预测控制 (MPC) 模型,提高了路径跟踪精度. 在模型中包括转向动力学,在模拟和现实世界测试中显著改善了控制器性能.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 机器人技术 机器人技术 机器人技术
背景情况:
- 汽车自动化方向盘需要精确的路径跟踪,以确保安全和效率.
- 现有的控制方法往往简化了车辆动态,可能会限制性能.
- 模型预测控制 (MPC) 为复杂的轨迹跟踪提供了一个有希望的框架.
研究的目的:
- 开发和评估一个模型预测控制 (MPC) 战略,用于自动化车辆转向路径跟踪.
- 调查不同方向盘动态模型 (一级与二级滞后) 对控制性能的影响.
- 在车辆的自我坐标系统中引入一种用于生成参考轨迹的新方法.
主要方法:
- 开发了一种包含转向动态的线性参数变化 (LPV) 车辆工厂模型.
- 实施了级联MPC结构,将转向动力学分为第二个MPC.
- 拟议的MPC方法和参考轨迹生成通过模拟和测试车辆进行了验证.
主要成果:
- 第一阶段的方向盘模型显示出比第二阶段的方向盘模型稍微更准确的路径跟踪.
- 在预测模型中包括转向动态,显著提高了控制器性能.
- 模拟和实验结果都证实了新型参考定义方法的有效性.
结论:
- 拟议的MPC方法有效地控制了自动驾驶汽车的方向盘,以准确跟踪路径.
- 包含详细的方向盘动态模型对于实现控制器高性能至关重要.
- 新的参考轨迹生成方法在实际应用中被证明是有效的.
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