基于自适应扩展卡尔曼波器的无刷直流电机的控制算法研究
Tong Jinwu1, Zha Lifan2, Lu Xinyun2
1Engineering Training Center, School of Applied Technology, Nanjing Institute of Technology, Nanjing 211167, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了自适应扩展卡尔曼波器 (AEKF),以改善无传感器无刷直流电机 (BLDC) 的状态估计. 在动态条件下,AEKF提高了准确性和稳定性,超过了传统的扩展卡尔曼波器 (EKF).
科学领域:
- 电气工程 电气工程
- 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:
- 传统的扩展卡尔曼波器 (EKF) 在无传感器无刷直流 (BLDC) 电机控制中因动态操作期间的模型不匹配而遭受性能下降.
- 在BLDC电机中,突然的转速和负载变化需要改进状态估计,以实现精确的控制.
- 现有的方法缺乏适应无传感器BLDC应用中的系统不确定性和外部干扰的能力.
研究的目的:
- 提出一个自适应扩展卡尔曼波器 (AEKF) 算法,以克服传统EKF在无传感器BLDC电机控制中的局限性.
- 在动态操作条件下增强转子位置和转速的状态估计准确性和稳定性.
- 为BLDC电机提供高动态性能无传感器控制的有效解决方案.
主要方法:
- 开发了一个自适应扩展卡尔曼波器 (AEKF) 算法,采用基于马哈拉诺比斯距离的强大权重策略.
- 实现了动态调整的适应性遗忘因子,用于在线更新创新协差.
- 在各种动态条件下模拟了AEKF算法,包括低速启动,速度变化和突然负载应用.
主要成果:
- 与传统的EKF相比,AEKF算法显著提高了转子位置和转速的估计精度.
- AEKF表现出加速的动态响应,减少了超越,并提高了干扰拒绝的稳定性.
- 在AEKF中观察到更好的适应系统不确定性和外部干扰的能力.
结论:
- 拟议的AEKF算法为动态条件下的无传感器BLDC电机控制提供了优越的状态估计解决方案.
- AEKF有效地解决了模型不匹配问题,并提高了整体系统性能和可靠性.
- 这项研究为在无传感器BLDC电机应用中实现高动态性能提供了有价值的工具.
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