A Novel Model Predictive Torque Control of SRMs With Low Measurement Effort

Abstract

This article presents a low-measurement effort and less storage space but an effective method to reduce the torque ripple. First, a torque-balanced measurement method is presented to obtain the flux-linkage characteristics at four torque-balanced positions. Then, a four-order Fourier series model is proposed to describe the entire flux-linkage and torque characteristics. To reduce the storage space, a Polynomial-Fourier series model is proposed to describe the torque and current model from the rotor position and flux-linkage. Based on the proposed Polynomial-Fourier series model, a novel model predictive torque control (MPTC) is implemented to minimize the torque ripple with flux-linkage-based torque estimation. Experimental results show the proposed method can effectively reduce torque ripple with lower measurement effort and less storage space compared with the traditional MPTC method. Furthermore, the key issues of model predictive control, such as error analysis, weight coefficients selection, and magnetic saturation, are discussed in detail. The proposed method provides a low-effort and convenient way to implement the advanced control of SRMs in the industry application.