无模型的电流控制解决方案采用智能控制,以提高电机驱动性能
1Automation and System Division, ESIGELEC, Rouen, 76800, France.
Scientific reports
|January 2, 2025
概括
本研究介绍了一种智能,无模型的电流控制方法,可以避免复杂的模型并减少错误. 数据驱动的方法提高了性能,提供了比传统技术更好的结果.
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:
- 传统模型预测电流控制 (MPCC) 是计算密集型的,并依赖于精确的工厂模型.
- 工厂参数的不确定性和模型差异降低了传统控制系统的性能.
研究的目的:
- 开发一种智能,无模型的电流控制策略,以减少计算负载并提高稳定性.
- 通过使用自适应粒子群集优化 (APSO) 来优化速度控制循环参数来提高动态性能.
主要方法:
- 使用数据驱动的方法,需要更少的输入特征,以提供高效的培训.
- 适应粒子群集优化 (APSO) 用于调整外部速度控制循环的增强参数.
- 对传统的控制方案进行比较分析,以验证拟议的方法.
主要成果:
- 无模型数据驱动的方法从基于模型的设计中准确地学习了切换状态,准确率为94.8%.
- 与传统方法相比,拟议的方法在稳定状态下表现优越.
- 在数据驱动控制方案中观察到较低的波扭曲和更高的稳定性.
结论:
- 提出的智能,无模型的电流控制策略有效地解决了工厂模型的不确定性,并减少了计算负担.
- 数据驱动的方法在性能,稳定性和效率方面比传统方法具有显著的优势.
- 集成APSO进一步提高了控制系统的动态性能.
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