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

Abstract

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.

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

“…Again, iron losses were considered for loss minimization through rotor flux reference adjustment, but they were neglected in the MPC algorithm, whereas magnetic saturation was not at all considered. A variant of the MPTC system with three PI controllers was considered in [19] for speed sensorless IM control. Both the speed observer and loss minimization algorithm included a frequency-dependent iron-loss resistance.…”

On some operating modes of a submersible induction electric motor

“…Определяющим фактором здесь становится необходимость управления ПЭД находящимся в условиях нестационарных воздействий, в том числе температурных [4,5]. Что, в конечном итоге, сводится к предиктивному управлению по математической модели ПЭД с непрерывной идентификацией ее параметров [6][7][8][9][10][11].…”

High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control

“…[31] Ref. Due to their significant benefits, predictive control methods have attracted the intention of many researches and they have been implemented in a wide range of applications, including power converters, actuator faults, pharmaceuticals industry, chemical processes, and induction motors [27][28][29][30][31][32][33][34][35][36][37][38]. Hence, in comparison with the conventional P&O algorithm, an improvement of 10.52% in the overall PV system efficiency was achieved by various research groups/researchers [27] via the application of the MPC technique.…”