根据IMMAKF对变速车辆,道路粗度等级和弹质量条件的适应性悬挂状态估计
Xiao Wu1, Wenku Shi1, Hong Zhang2
1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China.
Scientific reports
|January 19, 2024
概括
这项研究引入了适应式卡尔曼波器用于车辆悬挂系统,改善了在不断变化的驾驶条件下状态估计的准确性. 交互式多模型自适应卡尔曼波器 (IMMAKF) 增强了控制,并识别了道路状况.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 信号处理 信号处理
背景情况:
- 车辆悬架性能受到速度,道路状况和车辆质量等动态因素的显著影响.
- 传统的固定状态观察器在驾驶场景发生变化时努力保持准确性,这会影响悬挂控制和状态估计.
- 需要适应性控制策略来优化不同操作参数下的悬挂性能.
研究的目的:
- 开发一种先进的悬挂状态观察器,能够在驾驶条件的动态变化中进行准确的估计.
- 通过调整参数以实时的车辆速度,道路粗度和弹加速度来增强悬架控制.
- 调查拟议观察员识别道路粗度和估计悬挂弹质量的能力.
主要方法:
- 通过使用线性正方体调节器 (LQR) 和多目标优化算法设计了一个自适应控制悬挂系统.
- 导出了交互式多模型自适应卡尔曼波器 (IMMAKF) 的理论框架.
- 建立了两个基于IMMAKF的悬浮状态观察员和控制器,并在动态条件下进行模拟.
主要成果:
- 与标准IMMKF,自适应卡尔曼波器 (AKF) 和标准卡尔曼波器 (KF) 观察者相比,IMMAKF观察者显示出更高的估计准确性.
- 拟议的自适应式悬架控制器有效调整参数,以在不同的速度,粗度和质量条件下保持最佳控制.
- 模拟结果证实IMMAKF能够准确识别道路粗度等级,并估计悬架弹质量.
结论:
- IMMAKF提供了一个强大的解决方案,用于在变化的车辆动力学中准确估计悬挂状态.
- 适应式控制悬架系统通过动态调整环境和操作变量来确保最佳性能.
- IMMAKF提供了超越状态观测的扩展功能,使先进车辆系统能够识别道路状况和估计质量.
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