Nonlinear Model Predictive Speed Controller for Indirect Rotor Field Orientation of Induction Motor

Abstract

Electric vehicles (EVs) are vehicles that use electric motors in their traction system and install battery to power the motors. Due to cheap cost, less maintenance, very accurate speed regulation and for performance-oriented vehicles, induction motors (IMs) are used. The motor has different forms of control methods, whereas in this study, field-oriented control (FOC) is considered. FOC depends on the field orientation mechanism of the rotating frame. The rotating frame can be placed along either of the stator, rotor, or air gap fluxes. If it has been oriented on the rotor flux, it will be called rotor field oriented (RFOC). Hence, this will create a control structure that is based on alignment of the rotating frame along the rotor flux. In this research, indirect rotor field-oriented control (IRFOC) is taken into consideration and a better way of speed controller is developed. Since the model of induction motor is nonlinear, this research tries to develop nonlinear speed controller and see if the controller gives better response than the linear controller. The study selected nonlinear model predictive controller (NMPC). Due to its greater computational efficiency and well optimization of the system than its linear counterpart, NMPC ensured better performance than linear model predictive controller (LMPC), due to it included the exact model of the IM model and so that there will be no performance lost because of linearization. As internal current controller, two PI current controllers are developed and their controller gains are tuned using genetic algorithm (GA). The simulation results of NMPC, in the set point tracking case, the speed is well tracked with about 0.3 steady state error with rise and settling times 0.18s and 0.23s, respectively. There is 0.11 overshoot existing in the speed response. LMPC exhibits a steady state error equal to 2.30 and rise and settling times of 0.25s and 0.39s, respectively. The comparison between NMPC and LMPC demonstrates that the nonlinear controller suggests better speed tracking performance. In addition to the speed tracking, field weakening is well done by NMPC and the torque handling is also another quality of NMPC. In conclusion, NMPC has better overall performance quality when compared to LMPC and it is suggested that NMPC is suitable for IRFOC of IM