Speed Sensorless Control of a Bearingless Induction Motor Based on Modified Robust Kalman Filter

Bian, Yifan; Yang, Zebin; Sun, Xiaodong; Wang, Xiang

doi:10.1007/s42835-023-01649-y

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Based on this, a speed sensorless control strategy based on iterative center difference Kalman filter is proposed, which achieves good speed tracking performance. Reference [15] introduces control methods into extended Kalman filters and combines robust Kalman filters with model reference adaptive systems to obtain an improved robust Kalman filter; then, a speed sensorless control strategy based on the improved robust Kalman filter is proposed, which can effectively improve the accuracy of speed identification. The high-frequency signal injection method can improve the lowspeed observation performance, but it requires the motor to have a certain degree of salient polarity, and the signal extraction algorithm is complex.…”

Section: Introductionmentioning

confidence: 99%

Research on Sliding Mode Variable Structure Model Reference Adaptive System Speed Identification of Bearingless Induction Motor

Bu,

Tao,

Chen

2024

Energies

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To improve the speed observation accuracy of the bearingless induction motor (BL-IM) and achieve its high-performance speed sensorless control, an improved sliding mode variable structure model reference adaptive system speed identification method based on sigmoid function (sigmoid-VS-MRAS) is proposed. Firstly, to overcome the problem of initial values and cumulative errors in the pure integration link of the reference flux-linkage voltage model, the rotor flux-linkage reference voltage model has been improved by using an equivalent integrator instead of the pure integration link. Then, in order to improve the rapidity and robustness of speed identification, the sliding mode variable structure adaptive law is adopted instead of the PI adaptive law. In addition, in order to optimize the sliding mode variable structure adaptive law and overcome the sliding mode chattering problem, a sigmoid function with smooth continuity characteristics is used instead of the sign function. Finally, on the basis of the inverse system decoupling control of a BL-IM, simulation experiments were conducted to verify the sigmoid-VS-MRAS speed identification method. The research results indicate that when the proposed speed identification method is adopted, not only higher identification accuracy and rapidity can be achieved than traditional PI-MRAS methods, but it can also eliminate the problem of high-frequency vibration (with an amplitude of about 3.0 r/min) when using the sign-VS-MRAS method; meanwhile, the steady-state tracking speed with zero deviation can still be maintained after loading.

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Section: Introductionmentioning

confidence: 99%

Research on Sliding Mode Variable Structure Model Reference Adaptive System Speed Identification of Bearingless Induction Motor

Bu,

Tao,

Chen

2024

Energies

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“…Speed and position controllers are responsible for the generation of reference currents for current control. Other articles dealing with the topic of Bearingless Induction Motor are listed below [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

Dantas Neto,

Lopes,

Fonsêca

et al. 2024

Energies

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This article describes the study and digital implementation of a system onboard a TMS 3208F28335 ® DSP for vector control of the bearing motor speed with four poles split winding with 250 W of power. Smart techniques: ANFIS and Neural Networks were investigated and computationally implemented to evaluate the bearing motor performance under the following conditions: operating as an estimator of uncertain parameters and as a speed controller. Therefore, the MATLAB program and its toolbox were used for the simulations and the parameter adjustments involving the structure ANFIS (Adaptive-Network-Based Fuzzy Inference System) and simulations with the Neural Network. The simulated results showed a good performance for the two techniques applied differently: the estimator and a speed controller using both a model of the induction motor operating as a bearing motor. The experimental part for velocity vector control uses three control loops: current, radial position, and speed, where the configurations of the peripherals, that is, the interfaces or drivers for driving the bearing motor.

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“…However, the accuracy of these methods was constrained by the size and slope of their sliding mode surfaces, and as control requirements become increasingly stringent, the effectiveness of sliding mode control diminishes. In [5], the Kalman filters (KFs) were investigated, but the KFs can only be applied to linear systems. In order to apply the KFs to nonlinear systems, such as estimating the speed of a motor, it is necessary to linearize the system under study, resulting in the extended Kalman filter (EKF).…”

Section: Introductionmentioning

confidence: 99%

Research on Displacement Sensorless Control for Bearingless Synchronous Reluctance Motor Based on the Whale Optimization Algorithm–Elman Neural Network

Xu,

Zhao

2024

Actuators

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The unique structure of bearingless motors requires extra displacement sensors to monitor rotor movement, unlike conventional synchronous motors. However, this requirement inevitably escalates the cost and size of the motor. To address these issues, this paper proposes a novel approach: a bearingless synchronous reluctance motor (BSRM) without displacement sensors, utilizing the whale optimization algorithm–Elman neural network (WOA-ENN). The paper firstly introduces the suspension mechanism and mathematical model of the BSRM, upon which a function containing rotor position information is constructed. Subsequently, a sensorless method based on Elman neural network (ENN) is proposed, optimized using the whale optimization algorithm (WOA). Finally, the feasibility and reliability of the proposed approach are validated through simulations and experiments.

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Speed Sensorless Control of a Bearingless Induction Motor Based on Modified Robust Kalman Filter

Cited by 8 publications

References 32 publications

Research on Sliding Mode Variable Structure Model Reference Adaptive System Speed Identification of Bearingless Induction Motor

Research on Sliding Mode Variable Structure Model Reference Adaptive System Speed Identification of Bearingless Induction Motor

Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

Research on Displacement Sensorless Control for Bearingless Synchronous Reluctance Motor Based on the Whale Optimization Algorithm–Elman Neural Network

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