Zhu Xiaofeng, Hu Yiming, Yu Yinquan, Zeng Dequan, Yang Jinwen, Carbone Giuseppe
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330000, China.
Department of Mechanical, Energy, and Management Engineering, University of Calabria, 87036, Rende, Italy.
Sci Rep. 2024 Nov 4;14(1):26670. doi: 10.1038/s41598-024-77225-9.
Permanent magnet synchronous motor (PMSM) drive systems are receiving increasing attention due to their superior control quality and efficiency. Optimizing the control parameters are key to achieve the high-performance operation of PMSMs. Although heuristic algorithms demonstrate excellent optimization outcomes in simulations, there are still challenges in deploying optimization schemes in practical drives. In this study, a real-time online deployable control parameter optimization scheme is proposed. The optimization effect is evaluated through the system step response performance, and a framework for deploying optimization algorithms within the driver is developed. A fault suppression mechanism is also designed to mitigate overshoot and vibration issues caused by suboptimal solutions. The proposed scheme is validated on a rapid prototyping control platform. Experimental results confirm that the scheme exhibits good optimization performances across various operating conditions. The honey badger algorithm employed in this paper shows faster convergence and more stable optimization effects than other optimization algorithms. The optimization effect is improved by 2.2% and its performance in terms of consistency across multiple optimization results has increased by 40%.
永磁同步电机(PMSM)驱动系统因其卓越的控制质量和效率而受到越来越多的关注。优化控制参数是实现永磁同步电机高性能运行的关键。尽管启发式算法在仿真中展示了出色的优化结果,但在实际驱动器中部署优化方案仍存在挑战。在本研究中,提出了一种实时在线可部署的控制参数优化方案。通过系统阶跃响应性能评估优化效果,并开发了在驱动器内部署优化算法的框架。还设计了一种故障抑制机制,以减轻次优解引起的超调和振动问题。所提出的方案在快速原型控制平台上得到验证。实验结果证实,该方案在各种运行条件下均表现出良好的优化性能。本文采用的蜜獾算法比其他优化算法收敛更快,优化效果更稳定。优化效果提高了2.2%,其在多个优化结果一致性方面的性能提高了40%。