Sliding mode variable structure control strategy of bearingless induction motor based on inverse system decoupling

Bu, Wenshao; Zhang, Xiaofeng; He, Fangzhou

doi:10.1002/tee.22663

Cited by 30 publications

(18 citation statements)

References 35 publications

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“…The pole-pairs of torque winding and suspension winding in the motor stator slot are P M and P B , respectively, and the corresponding electrical angular frequencies are ω 1 and ω 2 . If two sets of windings meet the following condition: (1) P B = P M ± 1; (2) ω 1 = ω 2 , the controllable radial suspension forces can be generated [40], [41]. Fig.1 shows the principle of generating the radial suspension forces on the BIM.…”

Section: Principle and Mathematical Model Of The Bim A The Princmentioning

confidence: 99%

Rotor Vibration Control of a Bearingless Induction Motor Based on Unbalanced Force Feed-Forward Compensation and Current Compensation

et al. 2020

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To handle the unbalanced vibration caused by uneven rotor mass distribution in a bearingless induction motor (BIM), a control strategy that integrates unbalanced feed-forward compensation and current compensation is presented. Firstly, based on the analysis of the BIM rotor vibration mechanism and the unbalanced vibration influence on the performance of the BIM, the dynamic model of the rotor is derived. Secondly, an unbalanced force feed-forward compensation controller is designed to extract the synchronous vibration signals through the synchronous signal detection unit, and then the compensation force components are generated by unbalanced feed-forward compensation controller. In addition, considering the influence of current in the rotor induced by suspension winding, a current compensation link is applied to the system to enhance the suspension performance. Finally, a rotor unbalanced vibration compensation control system of the BIM is established. The simulation and experimental results show that the proposed compensation control strategy not only can effectively reduce the rotor radial displacement and suppress the unbalanced vibration, but also can improve the suspension performance. INDEX TERMS Bearingless induction motor (BIM), current compensation, feed-forward compensation, radial displacement, rotor eccentricity.

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Section: Principle and Mathematical Model Of The Bim A The Princmentioning

confidence: 99%

Rotor Vibration Control of a Bearingless Induction Motor Based on Unbalanced Force Feed-Forward Compensation and Current Compensation

et al. 2020

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“…In [13], by cascading the inverse model of the PMSM with the original PMSM system, a decoupling pseudo-linear system is constituted to achieve dynamic decoupling. In [14], a sliding mode variable structure (SMVS) control strategy based on the inverse system decoupling is proposed to achieve dynamic decoupling. However, there are many uncertain and time-varying control parameters which need to be adjusted by trial and error in these methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

et al. 2020

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In this paper, a novel control scheme with respect to the adaptive decoupling controller based on radial basis function neural network (ADEC-RBFNN) is developed. On one hand, in order to improve the system performance of the torque closed-loop control system (TCLCS) of the permanent magnet synchronous motor (PMSM) with the effects of the dynamic coupling and back electromotive force (EMF), we present a novel ADEC with which the TCLCS is asymptotically stable under Lyapunov stability theory. On the other hand, considering the uncertainty and time variant of both the PMSM and ADEC parameters, the RBFNN is utilized to optimize the ADEC parameters to achieve optimal system performance. Ultimately, experimental results demonstrate that the torque and current with the proposed control scheme have the good performance of small fluctuation and fast response in the whole ranges of the speed and torque, that is to say, the system with the proposed control scheme is with the good decoupling performance. INDEX TERMS Adaptive decoupling control, permanent magnet synchronous motor, radial basis function neural network, torque closed-loop control system.

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“…The windings generating the suspension force are superimposed on the stator of the motor, so that the suspension force winding and the armature winding of the motor are combined into a whole [8]. The basic principle of a bearingless motor can be realized in various conventional motors, such as induction motors, reluctance motors, permanent magnet motors, etc [9]- [12]. Among the various types of bearingless motors, the bearingless induction motor (BIM) has many merits such as simple construction, low cost and high mechanical strength, which has attracted extensive attention of scholars.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM

Cheng

Sun

et al. 2019

IEEE Access

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The flux-linkage characteristics of bearingless induction motors (BIMs) are nonlinear, and the models established by the general analytical method cannot accurately reflect the actual characteristics of BIMs. Thus, a novel method for nonlinear modeling of BIM flux linkage is proposed in this paper. The main objective of this method is to improve the accuracy of the flux linkage model based on the least square support vector machine (LSSVM) technique by applying the gray wolf optimization (GWO) algorithm to determine the optimal kernel parameter and regularization parameter of the LSSVM automatically. In this method, all BIMs flux linkage data are obtained from the finite-element method. In this paper, the relationship between input and output of the nonlinear flux linkage model is studied, and the precision model of GWO-LSSVM flux linkage is obtained. The simulation results demonstrate that the GWO-LSSVM model has high prediction accuracy and strong prediction ability. In addition, the GWO-LSSVM model is compared with other models. From this simulation comparison, it can be concluded that GWO-LSSVM modeling has the characteristics of higher accuracy. INDEX TERMS Bearingless induction motor, gray wolf optimization, least squares support vector machine, nonlinear model, finite element analysis.

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Sliding mode variable structure control strategy of bearingless induction motor based on inverse system decoupling

Cited by 30 publications

References 35 publications

Rotor Vibration Control of a Bearingless Induction Motor Based on Unbalanced Force Feed-Forward Compensation and Current Compensation

Rotor Vibration Control of a Bearingless Induction Motor Based on Unbalanced Force Feed-Forward Compensation and Current Compensation

Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM

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