volume | PIER Journals

Abstract

To achieve the dynamic decoupling of a permanent magnet-assisted bearingless synchronous reluctance motor (PMa-BSynRM), this study proposes an innovative decoupling control strategy. In this method, the inverse system is constructed by improving a genetic algorithm optimized fuzzy neural network to achieve decoupling control. Firstly, this article elucidates the structure and working principle of PMa-BSynRM, establishes a mathematical model, and conducts reversibility analysis. Secondly, by optimizing the fuzzy neural network through improved genetic algorithm, a system inverse is derived to achieve the decoupling of the initial system, transforming it into a linear-like system. Thirdly, the decoupling performance of the proposed control method for a 5-degree-of-freedom (5-DOF) system is validated through simulation. Finally, experimental validation is conducted on both 2-DOF and 3-DOF subsystems. Simulation results for the 5-DOF system and subsystem experiments indicate that the proposed method exhibits excellent control accuracy, rapid convergence, and dynamic anti-interference performance.

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Mr. Shenshen Sui

Dr. Yichen Liu

Prof. Huangqiu Zhu