Abstract: In predictive control of induction motors, control signals are adjusted by predicting the future behavior of the motor. These predictions are made on important parameters such as motor speed, current, and torque, and are supported by real-time data. In general, in model predictive control (MPC) method, the input and output of the system are optimized using cost functions based on reference values. Although the induction motor operates in continuous time, discretization methods are needed for performing the necessary current switching and feedback control with microprocessors. For reference speed tracking in this study, the stator current of a three-phase induction motor with specifications of 60 Hz, 50 HP, and 460 V was decomposed for the first time using Crank–Nicholson, Verlet integration, and Runge– Kutta Ralston methods, which are finite control set (FCS)-MPC discretization methods. In this paper, we compared the Forward Euler, Runge–Kutta 4, Runge–Kutta Ralston, Taylor series, Crank–Nicolson, and Verlet integration techniques, which are FCS-MPC methods, with the conventional discrete-time indirect field oriented control (IFOC) method for speed control of induction motors. Based on the simulation data obtained from the induction motor, overshoot, settling time, reference speed root mean square value, and total harmonic distortion values were taken into account. The Verlet integration method had the least settling time than other methods, in the range of 0–4 s, including nominal speed transitions. When the response signals were examined, it was seen that the Verlet integration method gave the lowest settling time and overshoot percentage values for the 4–8 s, while the Forward Euler method gave the lowest settling time and overshoot percentage values for the 8–10 s.
Cite this article as: Y. Koçak and N. Bayhan, “Discretization of stator current of induction motor using predictive control,” Electrica, 24(2), 489-502, 2024.