强化学习算法用于改善基于五相永磁同步电机模型预测控制的速度响应.
Ahmed M Hassan1,2, Jafar Ababneh3, Hani Attar4,5
1Department of Electrical Power and Machines Engineering, Faculty of Engineering, Benha University, Shoubra, Cairo, Egypt.
PloS one
|January 3, 2025
概括
一个新的强化学习 (RL) 控制算法提高了五相内部永磁同步电机 (5ph-IPMSM) 的性能. 这种RL-TD3方法与元启发式优化技术相比,提供了更高的速度响应.
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:
- 五相内部永磁同步电机 (5ph-IPMSMs) 对于电动汽车等先进应用至关重要.
- 优化控制性能对于这些系统至关重要.
研究的目的:
- 为5ph-IPMSM驱动器开发和评估一种新的强化学习 (RL) 控制算法.
- 在恒定扭矩和恒定功率区域中优化电机转速响应.
主要方法:
- 提出了一个基于双延迟深确定性政策梯度 (TD3) 的RL算法.
- 该RL算法在模型预测控制 (MPC) 框架内调整两个级联PI控制器.
- 性能与过境搜索 (TS),蜂蜜子算法 (HBA),矮人 (DM) 和花优化器 (DO) 相比进行了基准测试.
主要成果:
- 与四种元启发式优化技术相比,RL-TD3算法显示出更高的速度响应.
- 改进了关键绩效指标,包括结算时间,上时间,最大时间和超标百分比.
- 在所有测试方法中,RL-TD3实现了最小的沉时间.
结论:
- 拟议的RL-TD3控制策略显著提高了5ph-IPMSM驱动性能.
- 这种方法为优化电机转速控制提供了比当前的元启发技术更有效的方法.
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