Research on Displacement Sensorless Control for Bearingless Synchronous Reluctance Motor Based on the Whale Optimization Algorithm–Elman Neural Network

Abstract

The unique structure of bearingless motors requires extra displacement sensors to monitor rotor movement, unlike conventional synchronous motors. However, this requirement inevitably escalates the cost and size of the motor. To address these issues, this paper proposes a novel approach: a bearingless synchronous reluctance motor (BSRM) without displacement sensors, utilizing the whale optimization algorithm–Elman neural network (WOA-ENN). The paper firstly introduces the suspension mechanism and mathematical model of the BSRM, upon which a function containing rotor position information is constructed. Subsequently, a sensorless method based on Elman neural network (ENN) is proposed, optimized using the whale optimization algorithm (WOA). Finally, the feasibility and reliability of the proposed approach are validated through simulations and experiments.