A novel fuzzy flux sliding-mode observer for the sensorless speed and position tracking of PMSMs

Ye, Shuaichen

doi:10.1016/j.ijleo.2018.06.074

Cited by 18 publications

(6 citation statements)

References 19 publications

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“…The PMSM current state equations are established under the two‐phase stationary

α - β

coordinate system [13, 34], and it is assumed that the changing rate of the motor angular speed is negligible compared with the changing rate of the stator current, that is, the derivative of the rotor speed is approximately zero

{\overset{ω}{true}}_{e} \approx 0

[23].…”

Section: Fosmo Based On the Sigmoid Switching Functionmentioning

confidence: 99%

An improved permanent‐magnet synchronous machine sensorless drive based on the fuzzy‐PI phase‐locked loop and the adjustable boundary‐layer FOSMO

Yao

2020

IET Power Electronics

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This study proposes a variable boundary-layer full-order sliding-mode observer (FOSMO) with a fuzzy proportional plus integral (PI) phase-locked loop (PLL) for the sensorless control of a permanent-magnet synchronous machine (PMSM). The sigmoid switching function-based FOSMO, which includes feedback speed information, is presented and considered as the conventional method. To address the contradiction between the convergence performance and the chatter-reduction ability of the sliding-mode control, a fuzzy logic controller (FLC) is introduced to adjust the boundary-layer thickness according to the estimation error. Furthermore, a real-time parameter tuning module is integrated into the PI regulator of the conventional PLL to adjust its noise bandwidth. Hence, adverse effects of disturbance and external noise, especially under transient operations, can be eliminated. An overall PMSM sensorless field-oriented control scheme incorporating the proposed method is designed, and the corresponding experimental platform is constructed. Several groups of comparative experiments reveal that system chatters are largely reduced using the proposed method under both of the high-and low-speed running conditions and the dynamic anti-interference performance can also be significantly improved under load and speed disturbances. Therefore, the feasibility and effectiveness of the proposed variable boundary-layer FOSMO + fuzzy-PI PLL method are validated.

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“…The PMSM current state equations are established under the two‐phase stationary

α - β

{\overset{ω}{true}}_{e} \approx 0

[23].…”

Section: Fosmo Based On the Sigmoid Switching Functionmentioning

confidence: 99%

An improved permanent‐magnet synchronous machine sensorless drive based on the fuzzy‐PI phase‐locked loop and the adjustable boundary‐layer FOSMO

Yao

2020

IET Power Electronics

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“…Nowadays, combined fuzzy neural methods with sliding mode control [4,5], inverse control [6], robust control [7], H ∞ [8], adaptive estimator [9], etc. to control the speed and position of the permanent magnet synchronous motor are widely used.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Novel Intelligent Strategy for the Permanent Magnet Synchronous Motor Emulation

2022

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In this paper, an intelligent neural network-based controller is designed and implemented to control the speed of a permanent magnet synchronous motor (PMSM). First, the exact mathematical model of PMSM is presented, and then, by designing a controller, we apply the wind turbine emulation challenges. The designed controller for the first time is implemented on a Arm Cortex-M microcontroller and tested on a laboratory PMSM. Since online learning neural network on a chip requires a strong processor, high memory, and convergence guarantee, this article uses the offline method. In this method, first, for different work points, the neural network is trained by local controllers, and then, the trained network is implemented on the chip and used. Uncertainty in the parameters and the effect of load torque as challenges of control systems are applied in the proposed method, and a comparison with other methods is performed in the implementation results section.

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“…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%

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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A novel fuzzy flux sliding-mode observer for the sensorless speed and position tracking of PMSMs

Cited by 18 publications

References 19 publications

An improved permanent‐magnet synchronous machine sensorless drive based on the fuzzy‐PI phase‐locked loop and the adjustable boundary‐layer FOSMO

An improved permanent‐magnet synchronous machine sensorless drive based on the fuzzy‐PI phase‐locked loop and the adjustable boundary‐layer FOSMO

Design and Implementation of a Novel Intelligent Strategy for the Permanent Magnet Synchronous Motor Emulation

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

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