基于用于感应电机驱动器的神经树控制器的先进的直接扭矩控制
Oualid Aissa1, Abderrahim Reffas2, Abdelbasset Krama3
1LPMRN Laboratory, Faculty of Sciences and Technology, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, Algeria.
ISA transactions
|April 3, 2024
概括
本研究介绍了一种用于感应电机的新决策树直接扭矩控制 (T-DTC),显著减少扭矩和流量波动. 这种先进的方法提高了电机应用的控制精度和稳定性.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 传统的直接扭矩控制 (DTC) 遭受了显著的流量和扭矩波动.
- 歇斯底里控制器和传统的开关表限制了现有的DTC方法的性能.
研究的目的:
- 为感应电机引入一种基于决策树的新直接扭矩控制 (T-DTC) 策略.
- 与传统的DTC相比,大大减少了流量和扭矩波动.
- 为了提高感应电机控制的准确性和稳定性.
主要方法:
- 开发了一种使用人工神经网络和M5 Prime模型树的T-DTC方法.
- 用M5 Prime模型树取代传统的歇斯底里控制器.
- 使用C4.5分类算法实现了一个新的决策树表.
主要成果:
- 显著减少了流量和扭矩波动.
- 在感应电机控制中实现了更高的精度和稳定性.
- 通过MATLAB/Simulink模拟和实时HIL平台测试验证了T-DTC策略.
结论:
- 拟议的T-DTC战略比现有技术提供了显著的改进.
- T-DTC提供了一种更有效的方法来控制具有减少波纹的感应电机.
- 决策树和神经网络的集成显示了先进的运动控制的前景.
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