Velocity Sensor Fault-Tolerant Controller for Induction Machine Using Intelligent Voting Algorithm

Fadi Alyoussef, Ahmad Akrad, Rabia Sehab, Cristina Morel and Ibrahim Kaya
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Fadi Alyoussef: Electrical and Electronics Engineering Department, Institute of Natural and Applied Sciences, Dicle University, Diyarbakır 21280, Turkey
Ahmad Akrad: ESTACA’Lab, Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile, 53000 Laval, France
Rabia Sehab: ESTACA’Lab, Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile, 53000 Laval, France
Cristina Morel: ESTACA’Lab, Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile, 53000 Laval, France
Ibrahim Kaya: Electrical and Electronics Engineering Department, Faculty of Engineering, Dicle University, Diyarbakır 21280, Turkey

Energies, 2022, vol. 15, issue 9, 1-18

Abstract: Nowadays, induction machines (IMs) are widely used in industrial and transportation applications (electric or hybrid ground vehicle or aerospace actuators) thanks to their significant advantages in comparison to other technologies. Indeed, there is a large demand for IMs because of their reliability, robustness, and cost-effectiveness. The objective of this paper is to improve the reliability and performance of the three-phase induction machine in case of mechanical sensor failure. Moreover, this paper will discuss the development and proposal of a fault-tolerant controller (FTC), based on the combination of a vector controller, two virtual sensors (an extended Kalman filter, or EKF, and a sliding mode observer, or SMO) and a neural voting algorithm. In this approach, the vector controller is based on a new structure of a back-stepping sliding mode controller, which incorporates a double integral sliding surface to improve the performance of the induction machine in faulty operation mode. More specifically, this controller improves the machine performance in terms of having a fast response, fewer steady-state errors, and a robust performance in the existence of uncertainty. In addition, two voting algorithms are suggested in this approach. The first is based on neural networks, which are insensitive to parameter variations and do not need to set a threshold. The second one is based on fuzzy logic. Finally, validation is carried out by simulations in healthy and faulty operation modes to prove the feasibility of the proposed FTC.

Keywords: induction machine; mechanical sensor failure; fault-tolerant control; sensorless control; back-stepping controller; sliding mode observer; extended kalman filter; neural networks; fuzzy logic; voting algorithms; reliability; performance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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