Design and Implementation of Luenberger Model-Based Predictive Torque Control of Induction Machine for Robustness Improvement

Abstract

This letter proposes a Luenberger model-based predictive torque control (LM-PTC) of induction machine to compensate prediction error caused by mismatched parameters. In the traditional predictive torque control (T-PTC), stator current, stator flux vector, and electromagnetic torque are predicted in one sampling period by open-loop prediction model, which will inevitably lead to prediction error by mismatched parameters, first. Inspired by the idea of closed-loop Luenberger observer, in the torque and flux prediction, the feedback correction part is introduced into prediction equations for LM-PTC. Second, the steady prediction errors of T-PTC and LM-PTC are, respectively, analyzed with mismatched parameter. Finally, the proposed LM-PTC is verified by the comparison experiments including dynamic-state, transient-state, and steady-state experiments.