基于模型参数化的基因算法,使用速度信号和电机动态响应的稳定状态
Mayra Cruz-Fernández1,2, J T López-Maldonado1,2,3, Omar Rodriguez-Abreo1,2
1Division de Tecnologías Industriales, Universidad Politécnica de Querétaro, Santiago de Querétaro 76240, Mexico.
Biomimetics (Basel, Switzerland)
|March 26, 2025
概括
这项研究引入了一种新的方法,用于仅使用速度信号和稳定状态值的电机动力学模型参数化. 这种方法通过消除对多个传感器测量的需求,简化了电机分析,提高了工程应用的效率.
科学领域:
- * 电气工程和控制系统
- * 计算智能和优化
背景情况:
- *动态模型和参数化对于工程和工业应用中的电机至关重要.
- * 传统方法通常需要多个电机信号 (例如扭矩,电流,转速),需要复杂的仪器仪表.
- *现有的技术在参数估计准确性和测量要求方面存在局限性.
研究的目的:
- * 提出一种新的方法来参数化电机的动态模型,仅使用转速信号和稳定状态值.
- * 开发一种方法,减少对广泛传感器仪器仪表的需求,例如全电流或扭矩测量.
- *通过进化计算重建电机的机械和电学方程.
主要方法:
- *利用进化计算来重建电机的动态方程.
- * 使用电机转速信号的传输函数作为分析的主要输入.
- *使用速度和电流信号的根平均平方误差 (RMSE) 验证了模型的准确性.
主要成果:
- *成功地重建了电机的动态方程,对于转速和电流信号的RMSE低于1%.
- * 仅使用单个测量变量 (速度) 证明了参数估计的可行性.
- * 在没有直接测量电流或扭矩信号的情况下实现了准确的模型参数化.
结论:
- * 拟议的方法通过简化测量要求,在发动机动力学模型参数化方面取得了重大进展.
- *这种创新方法可以使用最小的传感器数据进行准确的系统识别,适用于各种电机控制场景.
- * 该技术为研究和工业中电机参数估计提供了成本效益和高效的替代方案.
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