无刷直流 (BLDC) 电机的优化系统识别 (SI) 使用数据驱动建模方法
Muhammad Aseer Khan1, Dur-E-Zehra Baig2, Husan Ali3
1Department of Electrical Engineering, Air University, Aerospace and Aviation Campus, Kamra, 43570, Pakistan. 215221@aack.au.edu.pk.
Scientific reports
|March 13, 2025
概括
这项研究模型无刷直流 (BLDC) 电机动力学,使用数据驱动的方法,如NARX. 先进的NARX模型实现了高精度,改善了BLDC电机控制和故障检测.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 无刷直流电机 (BLDC) 在各种应用中至关重要,但它们的非线性动力学带来了控制挑战.
- 精确的建模对于高效的控制,精确的操作和BLDC电机的早期故障检测至关重要.
- 现有的建模技术可能无法完全捕捉不同条件下的BLDC电机的复杂,非线性行为.
研究的目的:
- 用数据驱动方法研究和建模BLDC电机的非线性动力学.
- 为了比较最小平方 (LS) 方法和外源输入非线性自行回归网络 (NARX) 模型在BLDC电机系统识别方面的有效性.
- 在各种操作和噪声信号条件下评估开发模型的性能和稳定性.
主要方法:
- 使用MATLAB/Simulink系统识别,使用最小平方 (LS) 和NARX模型与可变回归器.
- 从无负载条件下的BLDC电机模拟中生成全面的数据集,并提供各种输入电压信号.
- 使用不同的数据集对LS和NARX模型进行培训和验证,然后对相同的信号进行基准测试.
- 在实时条件下测试模型的稳定性,包括升级/减速和噪音信号.
主要成果:
- 采用定制回归器的NARX模型显著超过了LS方法,达到99.1%的训练精度和98.01%的验证精度.
- 模型在预测BLDC电机动力学方面表现出有效性,包括转速响应和扭矩/转速波动.
- 在实时和噪音信号条件下测试时,NARX模型显示出稳健性和准确性.
结论:
- 具有定制回归器的NARX模型为模拟BLDC电机非线性动态提供了一种优越的方法.
- 精确的数据驱动模型,特别是NARX,可以增强反控制策略,改善BLDC电机的稳定性.
- 提出的建模技术为在BLDC电机应用中有效检测故障和提高性能提供了基础.
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