基于FNTSMC和适应性神经网络的复合控制为PMSM系统
Xiufeng Liu1, Yongting Deng2, Hongwen Li2
1Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Science, Changchun 130033, China; University of Chinese Academy of Sciences, Beijing 100049, China.
ISA transactions
|May 26, 2024
概括
一种新的自适应神经网络控制方法通过减少喋喋不休和提高对干扰的稳定性来提高永久磁铁同步电机 (PMSM) 的性能. 这种固定时间控制策略确保了更快的系统融合和稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 电气工程 电气工程
- 机器人技术 机器人技术 机器人技术
背景情况:
- 永久磁铁同步电机 (PMSM) 在各种应用中至关重要,但由于参数不匹配和外部干扰,容易导致性能下降.
- 传统的滑动模式控制方法经常受到高频聊天的影响,这限制了它们的实际实施.
研究的目的:
- 提出一种新的固定时间非单元终端滑动模式控制 (NFNTSMC) 方法,并为PMSM系统增强了自适应神经网络 (ANN).
- 为了提高动态性能,稳定性,并减少PMSM控制中的聊天.
主要方法:
- 固定时间非单元终端滑动模式控制器的设计,用于名义上的PMSM系统.
- 整合一个自适应的辐射基函数 (RBF) 神经网络,以在线近似和补偿一次性干扰.
- 使用Lyapunov方法进行稳定性和固定时间收分析.
主要成果:
- 拟议的NFNTSMC与ANN有效地接近未知的干扰,减少所需的切换增益.
- 观察到滑动模式聊天的显著减少,改善了系统的流性.
- 通过模拟和实验验证实了令人满意的动态性能和强大的稳定性.
结论:
- 开发的控制策略为PMSM系统提供了增强的动态性能和稳定性.
- 将ANN与NFNTSMC集成为缓解聊天和提高控制准确性的有效方法.
- 这项工作为设计用于PMSM应用的先进控制器提供了一个系统的框架.
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