活性干扰排斥控制与适应性RBF神经网络,用于永久磁铁球形电机
Xiwen Guo1, Ao Tan2, Qunjing Wang3
1School of Electrical Engineering and Automation, Anhui University, Hefei, China; National Engineering Laboratory of Energy-Saving Motor and Control Technology, Anhui University, Hefei, China; Engineering Research Center of Power Quality, Ministry of Education, Anhui University, Hefei, China.
ISA transactions
|November 22, 2024
概括
本研究介绍了用于永久磁体球形电机 (PMSpM) 的神经网络增强主动干扰排斥控制 (ADRC). 这种新的方法通过更好地处理干扰,提高了跟踪精度和稳定性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 在工程领域的人工智能.
- 电气工程 电气工程
背景情况:
- 永久磁体球形电机 (PMSpM) 面临着跟踪精度低,轨迹控制稳定性差的挑战.
- 现有的控制方法在严重的总干扰下与性能恶化作斗争.
研究的目的:
- 开发一个先进的控制方案,以提高PMSpMs的轨迹跟踪性能.
- 通过解决未知的总干扰来提高PMSpM控制系统的稳定性和准确性.
主要方法:
- 提出了一种与辐射基函数 (RBF) 神经网络集成的主动干扰拒绝控制 (ADRC) 方案.
- 该RBF神经网络近似未知的总干扰,与适应性法律更新权重.
- 一个非线性反循环补偿干扰,提高了扩展状态观察者的性能.
主要成果:
- 进行比较的模拟表明,RBF-ADRC方案具有优越的干扰排斥能力和稳定性.
- 在PMSpM测试平台上的实验验证证证了拟议的控制策略的有效性.
- 该方法确保了准确的轨迹跟踪,即使在显著的外部干扰下.
结论:
- 拟议的基于RBF神经网络的ADRC方案有效地提高了PMSpMs的跟踪精度和稳定性.
- 这种方法为在需要高精度和可靠性的应用中控制PMSpMs提供了有价值的解决方案.
- 该研究验证了先进控制策略的实际适用性和性能益处.
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