volume | PIER Journals

Abstract

To address the rotor vibration induced by rotor unbalance in a permanent magnet assisted bearingless synchronous reluctance motor (PMa-BSynRM), a feedforward compensation control method based on the Least Mean Squares (LMS) adaptive filtering algorithm, optimized by a Back Propagation Neural Network (BPNN), is proposed. Firstly, the operating principle of the PMa-BSynRM is introduced, and the mechanism of rotor unbalance vibration is analyzed. Secondly, a feedforward compensation controller is developed to extract the vibration signal and suppress rotor vibration. The BPNN is employed to adaptively adjust the LMS step size, thereby enhancing convergence speed, accuracy, and anti-interference capability. Furthermore, to overcome the inherent limitations of the BPNN, a hybrid optimization strategy that integrates particle swarm optimization (PSO) with an improved genetic algorithm (IGA) is adopted to optimize the initial weights and thresholds of the BPNN. Finally, a rotor unbalance vibration compensation control system for the PMa-BSynRM is established. Simulation and experimental results verify that the proposed control algorithm effectively reduces radial displacement and suppresses unbalanced vibration, while also exhibiting strong anti-interference performance and robustness.

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Mr. Tianliang Du

Prof. Huangqiu Zhu