Robust parallel predictive torque control with model reference adaptive estimator for im drives
Paper in proceeding, 2020

This paper presents the robustness improvement for the proposed parallel structure predictive torque control (PPTC) via a MRA-based estimator. Although predictive torque control (PTC) has the merits of lower switching frequency and straightforward implementation, it inevitably suffers from the inherent drawbacks of high torque ripple and inappropriate tuning of the weighting parameter. To solve this issue, the proposed PPTC employs two homogeneous objective terms which are optimized in a parallel strucutre, to bypass the usage of weighting parameters. However, the parameter mismatches in the control plant will lead to the prediction torque and flux error, which further impacts the control behavior of the system. Therefore, this paper evaluates the parameter sensitivity for PPTC, aiming to improve robustness of the proposed algorithm with a MRA-based parameter estimator. Finally, the validity of the proposed scheme is confirmed through an experimental assessment.

Parameter mismatch

Low torque ripple

Weighting factor optimization

Parallel predictive torque control


Haotian Xie

Technical University of Munich

Qian Xun

Chalmers, Electrical Engineering, Electric Power Engineering, Electrical Machines and Power Electronics

Ying Tang

Technical University of Munich

Fengxiang Wang

Chinese Academy of Sciences

Jose Rodriguez

Universidad Andrés Bello

Ralph Kennel

Technical University of Munich

Proceedings - 2020 International Conference on Electrical Machines, ICEM 2020

Vol. 23 August 2020 1219-1224 9271013

2020 International Conference on Electrical Machines, ICEM 2020
Virtual Gothenburg, Sweden,

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering


Control Engineering

Signal Processing



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