Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives

• Main Authors: Ahmed G. Mahmoud A. Aziz, Hegazy Rez, Ahmed A. Zaki Diab
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Mathematics
• Subjects: induction motor; model predictive; sensorless; high performance
• Online Access: https://www.mdpi.com/2227-7390/9/4/403

This paper introduces a novel sensorless model-predictive torque-flux control (MPTFC) for two-level inverter-fed induction motor (IM) drives to overcome the high torque ripples issue, which is evidently presented in model-predictive torque control (MPTC). The suggested control approach will be based on a novel modification for the adaptive full-order-observer (AFOO). Moreover, the motor is modeled considering core losses and a compensation term of core loss applied to the suggested observer. In order to mitigate the machine losses, particularly at low speed and light load operations, the loss minimization criterion (LMC) is suggested. A comprehensive comparative analysis between the performance of IM drive under conventional MPTC, and those of the proposed MPTFC approaches (without and with consideration of the LMC) has been carried out to confirm the efficiency of the proposed MPTFC drive. Based on MATLAB^‎®‎ and Simulink^® from MathWorks^® ‎(2018a, Natick, MA, 01760-2098 USA) simulation results, the suggested sensorless system can operate at very low speeds and has the better dynamic and steady-state performance. Moreover, a comparison in detail of MPTC and the proposed MPTFC techniques regarding torque, current, and fluxes ripples is performed. The stability of the modified adaptive closed-loop observer for speed, flux and parameters estimation methodology is proven for a wide range of speeds via Lyapunov’s theorem.