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PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2026-10-27
Physics-Informed Neural Network Inverse System Decoupling Control for Six-Pole Radial Hybrid Magnetic Bearings Optimized by Hybrid Particle Swarm Optimization Algorithm
By
Yuechu Ma,

    Mr. Yuechu Ma

    Jiangsu University

    China

    Homepage

Yichen Liu,

    Dr. Yichen Liu

    School of Electrical and Information Engineering

    Jiangsu University

    China

    Homepage

Huangqiu Zhu

    Prof. Huangqiu Zhu

    School of Electrical and Information Engineering

    Jiangsu University

    China

    Homepage

Progress In Electromagnetics Research C, Vol. 174, 11-21, 2026
DOI:10.2528/PIERC26090710 | Google Scholar

Abstract
To address severe radial coupling that significantly degrades rotor suspension precision and stability in six-pole radial hybrid magnetic bearings (HMBS), we propose a novel decoupling control that combines a physics-informed neural network (PINN) with a role-adaptive, heterogeneous, improved dynamic multi-swarm particle swarm optimization (HPSO) algorithm. First, we introduce the structure and working principle of the six-pole radial HMBS and derive its mathematical model using the Maxwell tensor method. Second, we construct an inverse system model with a PINN and propose an HPSO algorithm to resolve issues of initial weight sensitivity and gradient vanishing. Subsequently, we establish a six-pole radial HMBS simulation system to evaluate its floating, anti-interference, and decoupling performance. Finally, validation experiments on an experimental platform confirm the method's effectiveness and feasibility. Compared with the backpropagation neural network (BPNN) inverse system control, the proposed HPSO-PINN method reduces the coupling ratio by 37.74\%, effectively suppressing coupling in x- and y-directions.
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Citation
Yuechu Ma, Yichen Liu, and Huangqiu Zhu, "Physics-Informed Neural Network Inverse System Decoupling Control for Six-Pole Radial Hybrid Magnetic Bearings Optimized by Hybrid Particle Swarm Optimization Algorithm," Progress In Electromagnetics Research C, Vol. 174, 11-21, 2026.
DOI:10.2528/PIERC26090710
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