基于强大的类型控制器,ESO类型观察器,多个神经网络和RL-TD3代理的PMSM无感应控制的性能提升
Marcel Nicola1,2, Claudiu-Ionel Nicola1,2, Cosmin Ionete2
1Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了永久磁同步电机 (PMSM) 的强大的控制器,尽管参数和负载扭矩变化,但仍保持性能. 使用强化学习 (RL-TD3) 的改进系统显示出更高的控制精度和效率.
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:
- 永久磁铁同步电机 (PMSM) 容易因内部参数变化 (定子电阻,惯性) 和负载扭矩波动而降低性能.
- 经典的控制方法,如在场定向控制 (FOC) 中的PI控制器,在这种干扰下可能难以保持最佳性能.
研究的目的:
- 开发和验证PMSM强大的控制系统,能够处理参数和负载扭矩变化.
- 通过将强化学习 (RL-TD3) 代理与强大的控制器集成来提高PMSM控制性能.
- 评估拟议的控制策略与经典FOC相比,使用包括响应时间,速度波纹和碎形维度在内的指标.
主要方法:
- 一个强大的控制器的合成设计,以适应PMSM参数和负载扭矩的变化.
- 在MATLAB/Simulink中实现了强大的控制器和一个带有强化学习双延迟深确定性政策梯度 (RL-TD3) 代理的增强版.
- 开发四种观察者变体,结合神经网络和RL-TD3代理,用于估计PMSM旋转器转速和负载扭矩.
- 对传感器和无传感器控制策略的比较分析与经典的野外导向控制 (FOC).
主要成果:
- 强大的控制器有效地保持PMSM控制系统的性能,跨越一系列的参数和负载扭矩变化.
- 整合RL-TD3剂显著提高了传感器PMSM控制系统的性能.
- 数字模拟验证了RL-TD3训练剂在传感器和无传感器控制场景中的卓越性能.
- 分析表明,在拟议的高效控制系统中,旋转器转速信号的分数维度 (DF) 更高.
结论:
- 建议的强大的控制策略,增强RL-TD3药物,提供优越的性能,PMSM控制与传统的PI-basedFOC相比.
- 开发的观察器准确地估计了旋转器转速和负载扭矩,有助于提高控制精度.
- 这些发现支持了这样一个假设:更高效的控制系统会导致受控变量的分数维度更高,这表明动态行为得到了改善.
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