An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

Deepak Mohanraj; Devakirubakaran S; Praveen Kumar Balachandran

doi:10.1016/j.heliyon.2024.e39598

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

Heliyon. 2024 Oct 20;10(20):e39598. doi: 10.1016/j.heliyon.2024.e39598. eCollection 2024 Oct 30.

Authors

Deepak Mohanraj¹, Devakirubakaran S², Praveen Kumar Balachandran^{3

4}

Affiliations

¹ Department of Electrical and Electronics Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Kannampalayam post, Coimbatore, Tamil Nādu, 641402, India.
² Center for Smart Energy Systems, Chennai Institute of Technology, Kundrathur, Chennai, Tamil Nadu, 600069, India.
³ Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, India.
⁴ Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia.

Abstract

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K ₁ ) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

Keywords: Cost function; Dynamic modelling; Model predictive control; Switched reluctance motor drive; torque ripple.