基于模糊的MPC和机器学习状态观察者的自适应巡航系统
Jianhua Guo1, Yinhang Wang1, Liang Chu1
1College of Automotive Engineering, Jilin University, Changchun 130025, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了适应式巡航控制 (ACC) 系统的等级控制策略,以提高在不确定的驾驶条件下跟踪精度和稳定性. 这种新的方法通过先进的控制方法提高了车辆的安全性和舒适性.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 车辆智能化需要先进的控制系统,以提高安全性和舒适性.
- 传统的自适应巡航控制 (ACC) 系统在不确定和动态的驾驶环境中在跟踪性能,舒适性和稳定性方面存在局限性.
研究的目的:
- 提出一个分层的控制策略,以改善ACC系统在不确定的条件下的性能.
- 为了提高 ACC 系统的跟踪精度,乘客舒适度和控制强度.
主要方法:
- 基于深度学习的动态正常轮负载观察器被集成到ACC感知层中.
- 一种模糊模型预测控制 (fuzzy-MPC) 方法用于控制器设计,优化性能指标并适应不断变化的场景.
- 执行层使用了一个完全独立的PID控制器,以及基于规则的ABS控制方法,以提高安全性.
主要成果:
- 与模拟中的传统方法相比,拟议的层次控制策略显示出优越的跟踪精度和稳定性.
- 动态正常轮负载观察器有效地告知了车扭矩分配.
- 模糊的MPC控制器通过调整目标功能权重和约束来成功地适应不同的驾驶条件.
结论:
- 开发的层次控制策略显著提高了ACC系统的性能,提供了更好的安全性,舒适性和稳定性.
- 深度学习和模糊MPC的整合为未来的智能汽车控制系统提供了一个有希望的方向.
- 该战略有效地解决了在动态和不确定的驾驶场景中传统ACC系统的局限性.
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