A Composite Model Predictive Control Method of SRMs With PWM-Based Signal for Torque Ripple Suppression

IEEE Transactions on Transportation Electrification, vol. 10, pp. 2469–2478

Abstract

In this article, a novel composite predictive control using PWM control signals is proposed for the torque ripple suppression of the switched reluctance machine (sRm). A PWm modulation method for the asymmetric half-bridge (AHB) converter is introduced to achieve the conversion of the duty cycle to control signals. In the single-phase region, the deadbeat predictive control (DPC) is employed to predict the optimal duty cycle for the conduction phase. The model predictive control (MPC) using PWM control signals predicts the optimal duty cycles for the incoming phase and the outgoing phase in the commutation region. Latin-hypercube sampling (LHS) design is applied to obtain the appropriate duty cycle prediction scheme according to the machine operation state. The response surface model (RSM) is proposed to replace the cost function for achieving online optimization. The performance of the proposed composite predictive control method is evaluated by detailed simulation and experiment under different working conditions. The results show that the proposed control method exhibits better performance in the torque ripple suppression of SRM compared to the conventional MPC.

Authors 7

1. Lefei Ge

   Northwestern Polytechnical University

   Affiliation as printed

   School of Automation, Northwestern Polytechnical University, Xi’an, China

2. Jixuan Guo

   Northwestern Polytechnical University

   Affiliation as printed

   School of Automation, Northwestern Polytechnical University, Xi’an, China

3. Shuai Mao

   Northwestern Polytechnical University

   Affiliation as printed

   School of Automation, Northwestern Polytechnical University, Xi’an, China

4. Dianxun Xiao

   The Hong Kong University of Science and Technology (Guangzhou)

   Affiliation as printed

   Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

5. Dongdong Zhao

   Northwestern Polytechnical University

   Affiliation as printed

   School of Automation, Northwestern Polytechnical University, Xi’an, China

6. Shoujun Song

   Northwestern Polytechnical University

   Affiliation as printed

   School of Automation, Northwestern Polytechnical University, Xi’an, China

7. Rik W. De Doncker Aachen

   RWTH Aachen University

   Affiliation as printed

   ISEA, RWTH Aachen University, Aachen, Germany