基于自动干扰抑制的同步电感电动机鲁棒控制

IF 7.9 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of Industry Applications Pub Date : 2024-03-09 DOI:10.1109/OJIA.2024.3399009
Angelo Accetta;Maurizio Cirrincione;Filippo D'Ippolito;Marcello Pucci;Antonino Sferlazza
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摘要

本文提出了同步磁阻电机(SynRM)驱动器主动干扰抑制控制(ADRC)的理论发展和实验应用。ADRC 是输入输出反馈线性化控制 (FLC) 的稳健自适应扩展。它基于所谓的扩展状态观测器(ESO)对校正项的在线估计,通过对状态进行适当的非线性变换,对同步磁阻电机模型进行精确线性化。因此,任何未建模的动态或参数的不确定性都能得到妥善解决。该控制策略在数值模拟和实验中都得到了成功验证,在适当开发的测试装置上提供了 ADRC 对参数变化的鲁棒性,这是其他基于模型的非线性控制技术(如 FLC)无法实现的。仿真结果表明,即使在 SynRM 动态电感快速变化的情况下,ADRC 仍能保持其动态性能。实验结果证实了 ADRC 在面对任何模型参数不确定性时的鲁棒性。实验将所提出的 ADRC 与之前开发的 FLC(在调谐和失谐工作配置下)、传统的面向转子控制以及有限状态模型预测控制 (MPC)(在 MPC 中集成了速度控制)进行了比较。实验结果表明,该系统对任何参数变化都具有更好的鲁棒性。
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Robust Control of Synchronous Reluctance Motor Based on Automatic Disturbance Rejection
This article proposes the theoretical development and experimental application of the active disturbance rejection control (ADRC) to synchronous reluctance motor (SynRM) drives. The ADRC is a robust adaptive extension of the input-output feedback linearization control (FLC). It performs the exact linearization of the SynRM model by a suitable nonlinear transformation of the state based on the online estimation of the corrective term by the so-called extended state observers (ESO). Consequently, any unmodeled dynamics or uncertainty of the parameters are properly addressed. The control strategy has been verified successfully both in numerical simulations and experimentally on a suitably developed test set-up that provides the ADRC robustness versus parameters variations which cannot be obtained with other model-based nonlinear control techniques (e.g., FLC). Simulation results show the capability of the ADRC to maintain its dynamic performance, even in the presence of quick variations of the SynRM dynamic inductances. Experimental results confirm the robustness of the ADRC versus any model parameter uncertainty. The proposed ADRC has been experimentally compared with a previously developed FLC, in both a tuned and detuned working configuration, with the classic rotor oriented control, and with a finite state model predictive control (MPC), where speed control is integrated into the MPC. Experimental results show far better robustness versus any parameter variation.
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