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A rotor position estimator for switched reluctance motors using CMAC

机译：使用CMAC的开关磁阻电机的转子位置估计器

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This paper presents an approach to rotor position estimation in switched reluctance motors (SRMs) by using a cerebellum model articulation controller (CMAC). Previous research has shown that an artificial neural network (ANN) forms an efficient mapping structure through measurement of the flux linkages and currents for the phases. A CMAC is investigated in this paper in order to overcome the high computational power requirement problem that is encountered in a feedforward ANN based rotor position estimator. The CMAC structure does not contain neurons with activation functions, and all mathematical operations are performed without multiplication. These simplicities increase the throughput in real time implementation performed with conventional embedded controllers. However, the distributed memory structure of a CMAC requires more space. The issues involved in designing, training and implementing a CMAC are presented. In order to demonstrate the feasibility of the concept, a 20 kW, 6/4, three phase SRM is studied with training and evaluation data, which are obtained from a simulation program. A CMAC that is based on experimentally measured training and testing data for the same SRM is also used to demonstrate the promise of this approach.
机译：本文提出了一种通过使用小脑模型关节控制器（CMAC）来估计开关磁阻电机（SRM）中转子位置的方法。先前的研究表明，人工神经网络（ANN）通过测量相的磁链和电流来形成有效的映射结构。为了克服在基于前馈ANN的转子位置估计器中遇到的高计算能力要求问题，本文对CMAC进行了研究。 CMAC结构不包含具有激活功能的神经元，并且所有数学运算都无需乘法即可执行。这些简单性提高了使用常规嵌入式控制器执行的实时实施的吞吐量。但是，CMAC的分布式内存结构需要更多空间。介绍了设计，培训和实施CMAC所涉及的问题。为了证明该概念的可行性，研究了一个20 kW，6/4，三相SRM，其中包含从仿真程序获得的训练和评估数据。基于针对同一SRM的实验测量的训练和测试数据的CMAC也用于证明此方法的前景。

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