Experimental performance of a model reference adaptive flux observer based NFC for IM drive

M. Uddin, H. Wen, R. S. Rebeiro, M. Hafeez
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引用次数: 5

Abstract

This paper presents a model reference adaptive flux (MRAF) observer based neuro-fuzzy controller (NFC) for an induction motor (IM) drive. An improved observer model is developed based on a reference flux model and a closed-loop Gopinath model flux observer which combines current and voltage model flux observers. The d-axis reference flux linkage of the indirect field oriented control is provided by flux weakening method. Furthermore, a proportional-integral (PI) based flux controller is used to provide the compensation for the reference flux model by comparing the flux reference and the observed flux from Gopinath model flux observer. An improved self-tuned NFC is utilized as a speed controller for IM drive. The proposed NFC incorporates fuzzy logic laws with a five-layer artificial neural network (ANN) scheme. In the proposed NFC, parameters of the 4th layer are tuning online for the purpose of minimizing the square of the error. Furthermore, the design of normalized inputs makes the proposed NFC suitable for variant size of IM with a little adjusting. A complete simulation model for indirect field oriented control of IM incorporating the proposed MRAF observer based NFC is developed in Matlab/Simulink. The performances of the proposed IM drive is investigated extensively at different dynamic operating conditions such as step change in load, step change in change in speed, parameter variations, etc. The proposed IM drive is also implemented in real-time using DSP board DS1104 for a laboratory 1 HP IM. The performance of the proposed MRAF observer based NFC controller is found robust and potential candidate for high performance industrial drive applications.
基于NFC模型参考自适应磁链观测器的IM驱动实验性能
本文提出了一种基于模型参考自适应磁链观测器的感应电动机神经模糊控制器(NFC)。基于参考磁通模型和结合电流和电压模型磁通观测器的闭环Gopinath模型磁通观测器,建立了改进的观测器模型。采用磁链弱化法给出了间接磁场定向控制的d轴参考磁链。此外,通过比较参考通量与Gopinath模型通量观测器的观测通量,采用基于比例积分(PI)的通量控制器对参考通量模型进行补偿。采用改进的自调谐NFC作为IM驱动器的速度控制器。该方法将模糊逻辑规律与五层人工神经网络(ANN)相结合。在提出的NFC中,为了使误差的平方最小,在线调整了第四层的参数。此外,归一化输入的设计使得所提出的NFC适用于各种尺寸的IM,只需稍加调整。在Matlab/Simulink中建立了基于NFC的MRAF观测器的间接场定向控制的完整仿真模型。在负载阶跃变化、速度阶跃变化、参数变化等不同动态工况下,对所提出的IM驱动器的性能进行了广泛的研究。所提出的IM驱动器也在实验室1hp IM上使用DSP板DS1104实时实现。所提出的基于MRAF观测器的NFC控制器的性能被证明是鲁棒的,并且是高性能工业驱动应用的潜在候选。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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