Adaptive RBFNN Strategy for Fault Tolerant Control: Application to DSIM under Broken Rotor Bars Fault

Abstract

This paper presents a fault tolerant control (FTC) based on Radial Base Function Neural Network (RBFNN) using an adaptive control law for double star induction machine (DSIM) under broken rotor bars (BRB) fault in a squirrel-cage in order to improve its reliability and availability. The proposed FTC is designed to compensate for the default effect by maintaining acceptable performance in case of BRB. The sufficient condition for the stability of the closed-loop system in faulty operation is analyzed and verified using Lyapunov theory. To proof the performance and effectiveness of the proposed FTC, a comparative study within sliding mode control (SMC) is carried out. Obtained results show that the proposed FTC has a better robustness against the BRB fault.

DTC-SVM based on Interval Type-2 Fuzzy Logic Controller of Double Stator Induction Machine fed by Six-Phase Inverter

“…Over the last decade, Alternating Current (AC) motor the most used in industrial applications, these motors divided by the number of stator phases ( q) into several types: two-phase machines (q = 2), three-phase machines (q = 3) and multi-phase machines ( q > 3). Usually, multiphase machines replace three-phase machines in several applications such as; locomotive traction, electric vehicles, rolling mills, mine hoists and cement mills [1,3], because of this machine has high reliability, minimizing the rotor harmonic currents and minimized torque pulsations [2].…”

Enhancement of Space Vector Modulation Based-Direct Torque Control Using Fuzzy PI Controller for Doubly Star Induction Motor

“…The aim of our work is based on the utilization of a simple model of the DSIM, this machine has several advantages such as [3][4] reliability, robustness and meets the best criteria of behavior that the simple induction machine, because it reduces electromagnetic torque ripples and improves the power factor.…”