A look-up table-based model predictive torque control of IPMSM drives with duty cycle optimization

Mannan Hassan; Xinglai Ge; Abebe Teklu Woldegiorgis; Muhammad Shahid Mastoi; Muhammad Bilal Shahid; Rao Atif; Muhammad Suhail Shaikh; Shubash Kumar

doi:10.1016/j.isatra.2023.02.007

A look-up table-based model predictive torque control of IPMSM drives with duty cycle optimization

ISA Trans. 2023 Jul:138:670-686. doi: 10.1016/j.isatra.2023.02.007. Epub 2023 Feb 7.

Authors

Mannan Hassan¹, Xinglai Ge², Abebe Teklu Woldegiorgis³, Muhammad Shahid Mastoi⁴, Muhammad Bilal Shahid⁵, Rao Atif⁴, Muhammad Suhail Shaikh⁶, Shubash Kumar⁶

Affiliations

¹ Ministry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, School of Electrical Engineering, Southwest Jiao tong University, Chengdu 611756, China. Electronic address: mannan@my.swjtu.edu.cn.
² Ministry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, School of Electrical Engineering, Southwest Jiao tong University, Chengdu 611756, China. Electronic address: xlge@swjtu.edu.cn.
³ Ministry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, School of Electrical Engineering, Southwest Jiao tong University, Chengdu 611756, China; School of electrical engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia.
⁴ Ministry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, School of Electrical Engineering, Southwest Jiao tong University, Chengdu 611756, China.
⁵ Ministry of Education Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, School of Electrical Engineering, Southwest Jiao tong University, Chengdu 611756, China; Faculty of Engineering, Department of Electronic Engineering, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
⁶ Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.

PMID: 36781368
DOI: 10.1016/j.isatra.2023.02.007

Abstract

Model Predictive Control (MPC) is an effective method of driving motors and power converters due to its quick response, integrity, and multivariable control adaptability. A model-predictive torque control (MPTC) technique for permanent magnet synchronous motors (IPMSMs), which is computationally efficient and of low complexity, is presented in this paper. The proposed technique designs a lookup table that is independent of flux angle and torque deviation. For each control instant, this technique has to evaluate four voltage space vectors (VSV) from the lookup table, resulting in a substantial reduction in switching frequency and computational burden without compromising the performance. A maximum torque per ampere (MTPA) technique generates reference currents. The controller's complexity is minimized by eliminating the flux weighting factor from the cost function, saving time on offline weighting factor adjustments. Moreover, duty cycle optimization is performed using the mean torque control technique to minimize torque and flux ripples. The proposed method has been experimentally validated using a real-time simulator hardware in loop (HIL) with a TMS320F28335 floating-point digital signal processor on a prototype IPMSM drive. Furthermore, the proposed MPTC scheme is compared to conventional MPTC and direct torque control (DTC).

Keywords: Computational burden; Flux weakening; Interior permanent magnet synchronous motor; Maximum torque per ampere; Predictive torque control.