Comparison of Auto-Associative Models Based Sensor Compensation Methods Applied for Fault Tolerant Operation in Motor Drives

Abstract

Several approaches related to fault tolerant motor control have already been proposed. However, most of them consider the sensors fault-free and work about faults in motors and actuators. Sensors are the fundamentals in any feedback control system. The bad calibration of sensors in motor drives may lead to degradation of performance and even to instability. The purpose of this work is to evaluate some models presented in recent publications to perform on-line sensor fault compensation. In a standard fault tolerant approach, the fault would be detected and the sensor would be isolated. The faulted sensor may have an off- set or scaling error and could still be used if its error is compensated. In this paper, different mathematical solution based on auto-associative models will be evaluated and compared. This technique is described and applied in indirect vector control of an induction motor. Simulated and experimental results are discussed.

Keywords

• Auto-associative Models
• Drive Systems
• Induction Motor Control
• Sensor drift compensation