Dynamic performance improvement of induction motors used in rolling mill application: A two-loop model predictive control strategy

Abstract

In this paper, a novel two-loop model predictive control (TLMPC) is proposed to enhance the dynamic performance of induction motors used in rolling mill applications. In such applications, two individual voltage source inverters feed induction motors which are connected to the grid in a back-to-back manner. The grid-side converter, responsible for controlling the DC-link voltage, plays a vital role in the dynamic performance of the induction motors. Its undesired performance deteriorates the speed control of induction motors, which is a crucial need in the rolling mill industry. The proposed TLMPC includes a short-horizon finite set model predictive control in the inner loop to control the power flow by finding the best switching state of the grid-side converter. Additionally, a long-horizon continuous set model predictive control is developed in the outer loop for adjusting the set point value of the inner loop by predicting the DC-link voltage in a limited time horizon. An identification approach is exploited to approximate the non-linear model of the grid-side converter in order to use it in the outer loop. The mathematical proof of the robust stability of the proposed TLMPC is provided and its real-time execution is also certified. Finally, the capability of the proposed approach is evaluated using MATLAB/Simulink. A sensitivity analysis to evaluate the effect of the model’s inaccuracy and uncertainties on the performance of the proposed strategy is also provided.