基于改进的自适应MPC的智能车辆轨迹跟踪控制的研究
Wei Tan1, Mengfei Wang1, Ke Ma1
1Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究引入了一种改进的自适应模型预测控制 (AMPC) 方法,用于智能汽车轨迹跟踪. 新方法在复杂的驾驶条件下提高了准确性和稳定性,提供了更好的适应性和强度.
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:
- 智能车辆轨迹跟踪在复杂环境中面临着适应性,准确性和稳健性的挑战.
- 不确定的道路条件和动态驾驶场景限制了当前跟踪系统的性能.
研究的目的:
- 开发一种改进的自适应模型预测控制 (AMPC) 方法,用于增强智能汽车轨迹跟踪.
- 在不确定的和复杂的道路条件下提高跟踪精度和驾驶稳定性.
主要方法:
- 使用无气味的卡尔曼波器实时估计轮胎侧向力.
- 设计了一种适应性估计策略,用于轮胎拐角刚度.
- 开发了一个动态预测时间域自适应模型,优化了车辆速度和道路粘附.
主要成果:
- 在轨迹跟踪模拟中实现了低侧位置和方向角度错误.
- 在各种驾驶和道路粘附条件下,证明了高轨迹跟踪控制精度.
- 通过共同模拟验证了 AMPC 技术的有效性.
结论:
- 改进的AMPC方法显著提高了智能车辆轨迹跟踪性能.
- 这种方法为智能车辆控制系统提供了更好的适应性和稳定性.
- 为优化智能车辆跟踪控制系统提供了宝贵的参考.
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