Displacement Self-Sensing Control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based on Least Square Support Vector Machine Optimized by Improved NSGA-II

In order to solve the problems of low integration, low reliability, and high cost caused by mechanical sensors used in bearingless synchronous reluctance motor (PMa-BSynRM) control system, a novel displacement self-sensing control method using a least square support vector machine (LSSVM) left inverse system is proposed. First, the working principle of the PMa-BSynRM is introduced and the mathematical model of the PMa-BSynRM is derived. Second, the observation principle of the left-inverse system of the PMa-BSynRM is explained and the left-invertibility of the displacement subsystem is proved. Thirdly, the improved NSGA-II algorithm is utilized to optimize the regularization parameter and the bandwidth of LSSVM, and the displacement self-sensing control system is constructed. The simulations of speed variation and anti-interference are performed, which proves the dynamic tracking performance of the displacement. Finally, the static suspension, speed variation and anti-interference experiments are carried out. The feasibility and reliability of the proposed displacement self-sensing method are verified.