Ultra-local dual-torque model for model-free predictive torque control without weighting factors in induction motor drives

Abstract

Finite Control Set Model Predictive Control (FCS-MPC) primarily faces challenges related to parameter sensitivity and weighting factor design. To address these, this article proposes an ultra-local dual-torque model to establish a model-free predictive torque control (PTC) method without weighting factors for induction motor (IM) drives. Derived from the dynamic equations of electromagnetic and reactive torques, the proposed model can simplify the multivariable control of torque and flux in conventional PTC by focusing on univariate torque control. Since the torque prediction is performed directly rather than indirectly, the complexity of developing two independent ultra-local models of stator flux and current in model-free PTC is avoided. The constructed cost function relies on the torque and its dual quantity, avoiding the need for tuning the weighting factor. To enhance robustness against parameter mismatches, a linear extended state observer (LESO) is employed to identify both known and unknown system parts. Additionally, a novel flux observer is designed to mitigate low-speed performance degradation due to stator resistance. The proposed method is compared with conventional PTC and model-free PTC. The simulation and experimental results demonstrate that the proposed method has superior performance in parameter adaptability, torque ripple minimization, current harmonic reduction, and effective very low-speed operation.