Self-Sensing Technology of Rotor Displacement for Six-Pole Radial Active Magnetic Bearing Using Improved Quantum Particle Swarm Optimized Cubature Kalman Filter

Sun, JingBo; Zhu, Huangqiu; Liu, Gai

doi:10.1109/jestpe.2021.3118491

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…The anti-alias filter in this article is a second-order low-pass Butterworth filter, and its −3-dB cut-off frequency is designed at 10 kHz. According to (6) and the parameters in Table II, the magnitude and phase frequency response of the designed sensor are presented in Fig. 9.…”

Section: Methodsmentioning

confidence: 99%

“…The self-sensing approach abandons the integration of physical position sensors and extracts the position information from the electromagnetic signals in the bearing actuators [3]- [5]. However, it is most often at the expense of a loss of system robustness and increased controller complexity [2], [6]. Most dedicated state-of-the-art AMBs still use dedicated position sensors, usually including inductive and eddy current sensors.…”

Section: Introductionmentioning

confidence: 99%

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Driver Circuit Design for a New Eddy Current Sensor in Displacement Measurement of Active Magnetic Bearing Systems

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Driver Circuit Design for a New Eddy Current Sensor in Displacement Measurement of Active Magnetic Bearing Systems

et al. 2022

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“…Due to the asymmetry of the structure, the relationship between the suspension force and current is nonlinear, and there is a strong coupling between the degrees of freedom. To improve these problems and further increase bearing capacity, a six-pole axial-radial active magnetic bearing (AR-AMB) with a symmetrical structure is proposed [6,7].…”

Section: Introductionmentioning

confidence: 99%

Analytical Model of Six-pole Axial-radial Active Magnetic Bearing Based on Flux Density and Segmentation of Magnetic Field

Zhu,

Wang,

Liu

2024

PIER M

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To reduce the coupling resulting from structural asymmetry and enhance the load-bearing capacity per unit area, a six-pole axial-radial active magnetic bearing (AR-AMB) has been suggested. To refine the precision of the mathematical model derived from the conventional equivalent magnetic circuit model, a modeling technique that employs flux density and magnetic field segmentation has been proposed. Firstly, the structure and operational principle of the six-pole AR-AMB are introduced. Subsequently, an improved model based on the flux density is established by considering the internal relationship between the iron core and air gap magnetic field in a magnetic bearing with pole shoes. The model addresses issues related to the accurate calculation of fringing magnetic flux and magnetic saturation of core materials while accounting for eddy current effects on suspension force. Finally, the accuracy of the theoretical analysis results has been validated through finite element simulation and experiment, and demonstrated that the rotor based on this model exhibits robust anti-interference capabilities.

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“…Scholars have proposed a variety of displacement selfdetection methods for bearingless motors. These include the classical high-frequency injection method [10][11][12], Kalman filter numerical simulation method [13], volumetric Kalman filter (CKF) rotor displacement prediction method under the optimization of Improved Quantum Particle Swarm Optimization (IQPSO) algorithm [14], and reciprocal inductive current detection rotor displacement estimation method [15]. Model Reference Adaptive System (MRAS) based rotor displacement estimation method depends on the accuracy of the model [16].…”

Section: Introductionmentioning

confidence: 99%

Displacement Self-sensing Control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based on BP Neural Network Optimized by Improved PSO

Wang,

Liu,

Zhu

2024

PIER C

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In order to solve the problems of low reliability, low integration, and high cost brought by mechanical sensors in the control system of permanent magnet-assisted bearingless synchronous reluctance motor (PMa-BSynRM), a displacement self-sensing method of the back propagation (BP) neural network left-inverse system under the optimization of an improved particle swarm algorithm is proposed. Firstly, the working principle of PMa-BSynRM is introduced, and the mathematical model of PMa-BSynRM is derived. Secondly, the suspension force model is established to prove the left reversibility of the PMa-BSynRM displacement subsystem on the basis of the observation principle of the left reversible system. Thirdly, the weights of BP neural network are optimized by using the improved particle swarm algorithm to avoid local optimum, and the final weights are obtained to complete the construction of the displacement self-detection control system. On this basis, velocity change and anti-interference simulations are conducted to prove the tracking performance of the displacement system. Finally, static suspension, velocity change and anti-interference experiments are executed which verify the accuracy and feasibility of the proposed displacement self-detection system.

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Self-Sensing Technology of Rotor Displacement for Six-Pole Radial Active Magnetic Bearing Using Improved Quantum Particle Swarm Optimized Cubature Kalman Filter

Cited by 8 publications

References 16 publications

Driver Circuit Design for a New Eddy Current Sensor in Displacement Measurement of Active Magnetic Bearing Systems

Driver Circuit Design for a New Eddy Current Sensor in Displacement Measurement of Active Magnetic Bearing Systems

Analytical Model of Six-pole Axial-radial Active Magnetic Bearing Based on Flux Density and Segmentation of Magnetic Field

Displacement Self-sensing Control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based on BP Neural Network Optimized by Improved PSO

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