Proposed Fault Detection Algorithm with Optimized Hybrid Speed Control

Abstract

The Brushless DC (BLDC) motor is a common choice for industrial applications, particularly in the automotive sector, owing to its high efficiency and robust capabilities. To detect the position of the motor rotor, hall-effect sensors can be used, but these sensors may prevent the system from operating if they fail. Consequently, fault-tolerant control (FTC) has been proposed in several studies to ensure continuity of operation in the event of sensor failure. This paper proposes an innovative method of fault detection in the hall effect sensor for a BLDC motor using combinatorial functions. This paper proposes an innovative method of hall-effect sensor fault detection for a BLDC motor using combinatorial functions. For the speed control of the BLDC under study, a hybrid adaptive neuro-fuzzy inference control (ANFIS) is implemented. In addition, the FTC signal reconstruction technique adopted has been improved to achieve motor start-up despite a fault in one of the sensors, thanks to well-defined fault detection algorithms. Simulation results are presented for each sensor failure case to test the effectiveness of the method used.