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基于MPC的自动驾驶汽车路径跟踪控制器的数据驱动增强
Jianhua Guo1, Zhihao Xie1, Ming Liu2
1National Key Laboratory of Automotive Chassis Integration and Bionics, Changchun 130022, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究通过使用数据驱动方法来增强自动驾驶汽车路径跟踪. 新方法提高了准确性,在具有挑战性的条件下超过标准控制器.
科学领域:
- 自主系统 自主系统
- 控制理论 控制理论
- 机器学习 机器学习
背景情况:
- 准确的控制模型对于自动驾驶汽车的基于模型的控制至关重要.
- 由于简化,参数变化和噪音降低路径跟踪精度而导致的模型不准确性.
研究的目的:
- 为自动驾驶汽车的基于模型预测控制 (MPC) 的路径跟踪控制器 (DD-PTC) 引入数据驱动的增强功能.
- 通过提高控制模型准确度来提高车辆路径跟踪的精度.
主要方法:
- 使用Kolmogorov-Arnold网络 (KANs) 来估计轮胎侧向力和纠正动态预测模型的拐角刚度.
- 采用高斯过程回归 (GPR) 来捕捉未建模的车辆动态,用于全面的控制模型.
- 在方向盘控制的MPC框架内实施增强型号.
主要成果:
- 与标准的MPC和线性二次调节器 (LQR) 策略相比,DD-PTC方法显示出更高的性能.
- 观察到横向距离错误的显著减少,特别是在具有挑战性的驾驶场景下.
- 验证使用Simulink-CarSim模拟平台进行.
结论:
- 拟议的数据驱动增强有效地提高了自动驾驶汽车路径跟踪控制模型的准确性.
- DD-PTC为精确的方向盘控制提供了强大的解决方案,性能优于传统方法.
- KAN和GPR的整合提供了一个强大的框架,用于解决自动驾驶中的模型不确定性.
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