FPGA-realization of an RBF-NN tuning PI controller for sensorless PMSM drives

Than, Hoang; Kung, Ying‐Shieh

doi:10.1007/s00542-019-04347-w

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Based on the estimated back EMF, the rotor position is directly determined by the arctangent function in the traditional methodθ e = arctan ê α e β (5) and the electrical rotor speed can be obtained byω e = dθ e dt . However, due to using the arctangent function, the estimated position and speed are vulnerable to noise and harmonics.…”

Section: Speed and Position Estimation Methodsmentioning

confidence: 99%

“…To solve these shortcomings, sensorless control techniques have been studied. Since the back electromotive force (EMF) provides the information of rotor position and speed, back EMF-based estimators are widely proposed, such as the Luenberger observer [1,2], the extended Kalman filter (EKF) observer [3][4][5], the model reference adaptive system (MRAS) observer [6,7], or the sliding mode observer (SMO) [8][9][10][11][12][13][14][15]. Among those observers, SMO is the most applicable because it has simple structure, robustness against disturbance, low sensitivity to parameter perturbations, and easy implementation.…”

Section: Introductionmentioning

confidence: 99%

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Realization of the Neural Fuzzy Controller for the Sensorless PMSM Drive Control System

Hoai

Chen

Chang³

2020

Electronics

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A neural fuzzy controller (NFC)-based speed controller for the sensorless permanent magnet synchronous motor (PMSM) drive control system is realized in this paper. The NFC is a fuzzy logic controller (FLC), which adjusts the RBFNN-based (radial basis function neural network) parameter by adapting the dynamic system characteristics. For sensorless PMSM drive, the integration of sliding mode observer (SMO) and phase-locked loop (PLL) is executed to estimate the rotor position and speed. To eliminate the initial rotor position estimation and overcome the conventional PLL-based position estimation error in the direction reversion transition, the I-f control strategy is applied to start up the motor and change the rotational direction effectively. The system performance was verified in various experimental conditions. The simulation and experimental results indicate that the proposed control algorithm is implemented efficiently. The motor starts up with diverse external loads, operates in a wide speed range for both positive and negative directions, and reverses the rotational direction stably. Furthermore, the system presents robustness against disturbance and tracks the command speed properly.

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Section: Speed and Position Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Realization of the Neural Fuzzy Controller for the Sensorless PMSM Drive Control System

Hoai

Chen

Chang³

2020

Electronics

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“…However, using these sensors will lead to additional mounting space, higher cost and less reliability from the perspective of environmental constraints (Zhao et al , 2013). To overcome these limitations, various sensorless control techniques based on back electromotive force (EMF) estimation are proposed, such as extended Kalman filter based estimation, (Than and Kung, 2019; Zhang and Cheng, 2016) or sliding mode observer (SMO)-based estimation approaches (Chen et al , 2015; Lu et al , 2019; Wang et al , 2019; Calgan, 2022). The extended Kalman filter method has a computational complexity because of the recursive parameter estimation, while the SMO delivers high accuracy, robust estimation and low sensitivity to parameter variations (Chen et al , 2015).…”

Section: Introductionmentioning

confidence: 99%

Sensorless robust control for PMSG wind energy conversion systems based on smith predictor ADRC and SMO-PLL estimation

Benrabah,

Khoucha,

Raza

et al. 2023

COMPEL

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Purpose The purpose of this study is to improve the control performance of wind energy conversion systems (WECSs) by proposing a new sensorless, robust control strategy based on a Smith predictor active disturbance rejection control (SP-ADRC) associated with a speed/position estimator. Design/methodology/approach The estimator consists of a sliding mode observer (SMO) in combination with a phase-locked loop (PLL) to estimate the permanent magnet synchronous generator (PMSG) rotor position and speed. At the same time, the SP-ADRC is applied to the speed control loop of the variable-speed WECS control system to adapt strongly to dynamic characteristics under parameter uncertainties and disturbances. Findings Numerical simulations are conducted to evaluate the speed tracking performances under various wind speed profiles. The results show that the proposed sensorless speed control improves the accuracy of rotor speed and position estimation and provides better power tracking performance than a regular ADRC controller under fast wind speed variations. Practical implications This paper offers a new approach for designing sensorless, robust control for PMSG-based WECSs. Originality/value A new sensorless, robust control is proposed to improve the stability and tracking performance of PMSG-based WECSs. The SP-ADRC control attenuates the effects of parameter uncertainties and disturbances and eliminates the time-delay impact. The sensorless control design based on SMO and PLL improves the accuracy of rotor speed estimation and reduces the chattering problem of traditional SMO. The obtained results support the theoretical findings.

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“…In some cases, the sensors are environment-sensitive, reducing control reliability and adaptiveness. To solve these shortcomings, various back-EMF-based (back electromotive force based) sensorless control techniques are designed and applied, such as the extended Kalman filter (EKF) approaches [1][2][3] or the sliding mode observer (SMO) approaches [4][5][6][7]. The EKF involves a lot of recursive computations because it consists of prediction and innovation.…”

Section: Introductionmentioning

confidence: 99%

Realization of the Sensorless Permanent Magnet Synchronous Motor Drive Control System with an Intelligent Controller

Hoai

Chen

Than³

2020

Electronics

Self Cite

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This paper presents the sensorless control algorithm for a permanent magnet synchronous motor (PMSM) drive system with the estimator and the intelligent controller. The estimator is constructed on the novel sliding mode observer (SMO) in combination with a phase-locked loop (PLL) to estimate the position and speed of the rotor. The intelligent controller is a radial basis function neural network (RBFNN)-based self-tuning PID (Proportional-Integral-Derivative) controller, applied to the velocity control loop of the PMSM drive control system to adapt strongly to dynamic characteristics during the operation with an external load. The I-f startup strategy is adopted to accelerate the motor from standstill, then switches to the sensorless mode smoothly. The control algorithm program is based on MATLAB and can be executed in simulations and experiments. The control system performance is verified on an experimental platform with various speeds and the dynamic load, in which the specified I-f startup mode and sensorless mode, inspected by tracking response and speed regulation. The simulation and experimental results demonstrate that the proposed method has worked successfully. The motor control system has smooth switching, good tracking response, and robustness against disturbance.

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FPGA-realization of an RBF-NN tuning PI controller for sensorless PMSM drives

Cited by 7 publications

References 8 publications

Realization of the Neural Fuzzy Controller for the Sensorless PMSM Drive Control System

Realization of the Neural Fuzzy Controller for the Sensorless PMSM Drive Control System

Sensorless robust control for PMSG wind energy conversion systems based on smith predictor ADRC and SMO-PLL estimation

Realization of the Sensorless Permanent Magnet Synchronous Motor Drive Control System with an Intelligent Controller

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