Observer-Predictor-Based Predictive Torque Control of Induction Machine for Robustness Improvement

In the finite control set model predictive control (FCS-MPC) of induction motors, the mismatched motor parameters will inevitably lead to model predictive errors, and then reduce the control performance. At present, the evaluation methods of the prediction errors caused by mismatched parameters and of improving robustness have become the research hotspots of FCS-MPC of induction motors. In this article, an observer-predictor-based predictive torque control of induction machine is proposed in order to improve the prediction accuracy. In the stator current prediction equation, this article abandons the traditional open-loop prediction equation and proposes a closed-loop stator current prediction equation. In the stator flux prediction equation, this article abandons the traditional stator flux prediction model based on voltage model, and proposes a discrete hybrid prediction model combining voltage model and current model, where the proportional-integral regulator is used to switch the voltage model to the current model. Second, the stability and parameter design principles of the closed-loop stator current prediction model and the closed-loop stator flux prediction model are analyzed. Finally, the experimental results show that the proposed method is superior to the traditional method in dynamic performance, steady-state performance, and parameter robustness.