Decoupling Control of Permanent Magnet Synchronous Motor With Support Vector Regression Inverse System Method

Xie, Miao; Xie, Liang-Liang

doi:10.1109/access.2020.3039053

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Taking the built-in permanent magnet synchronous motor as the research object, the vector control technology of the built-in permanent magnet synchronous motor is analyzed and studied. Firstly, the mathematical model of the permanent magnet synchronous motor is established, ignoring the electronic hysteresis and eddy current loss, the core reluctance and saturation of the motor stator and rotor, and there is no damping winding on the motor rotor [15,16]. The back EMF wave of the three-phase winding of the motor is a sine wave.…”

Section: Design Of Sensorless Control System For Permanent Magnet Syn...mentioning

confidence: 99%

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

2022

IJM

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In recent years, the application range of electric energy in modern industry has gradually expanded. Permanent magnet synchronous motor has the characteristics of high efficiency and energy saving and has obvious advantages in traction application. In order to achieve good vector control of permanent magnet synchronous motor in the full speed range, Research on model reference adaptive system (MRAS) and pulsating high frequency injection method to construct the permanent magnet synchronous motor vector control system and combined with the flux weakening control algorithm to control the motor under the condition of limited inverter output, so as to realize the sensorless vector control of the motor in the full speed range. The difference between the estimated value of the algorithm and the actual value is compared on the actual experimental platform to verify the feasibility of the control algorithm. The experimental results show that, under medium and high-speed working conditions, the motor speed and rotor position tracking accuracy of MRAS algorithm is high, and the tracking error is less than 0.01rad. The pulsating high-frequency injection method can accurately track the permanent magnet synchronous motor under lowspeed working conditions, the speed error is less than 1n / R / min, and the rotor position error is less than 0.03rad. The static and dynamic performance of the control system is good, It can better deal with the sudden change of motor load. Using MRAS algorithm and pulsating high frequency injection method to control permanent magnet synchronous motor in full speed range is of great significance to improve the speed control performance of permanent magnet synchronous motor.

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Section: Design Of Sensorless Control System For Permanent Magnet Syn...mentioning

confidence: 99%

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

2022

IJM

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“…In the decoupling strategy of the NNIS, the key is the design and construction of the neural network, but the relevant research and literature have not been discussed too much. A typical error in the back-propagation feed-forward neural network (BPNN) is selected in many documents to identify the inverse system (Bu et al, 2019b;Xie and Xie, 2020). There is no detailed description on how to select the parameters and algorithm in the BPNN.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

Gong

Zhi-Qiang²,

Wang³

et al. 2022

Front. Energy Res.

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This study obtains the analytical inverse system of a permanent magnet synchronous motor (PMSM) model based on the traditional magnetic field orientation decoupling control mode by analyzing the inverse quality of the PMSM. Using the neural network’s excellent approximation ability and well learning functions, a neural network inverse system (NNIS) of the decoupling control system was established by identifying and offline training the back propagation neural network (BPNN) and radial basis function neural network (RBFNN). The data collected from the analytical inverse system of the PMSM model are used to analyze and compare the prediction accuracy and running time of the neural network, so as to optimize the structure and parameters of the neural network. The simulation results of three PMSM decoupling control systems show that the PMSM decoupling control system based on RBF NNIS has good dynamic and static decoupling performance, and robustness.

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“…Support vector regression (SVR) is widely applied for regression problems due to its ability of converting the original low-dimensional problem to a high-dimensional kernel space linear problem by introducing kernel functions [1,2]. e empirical risk and the confidence interval are balanced by adopting the principle of Structural Risk Minimization [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Feature Weighting Method for Support Vector Regression

Xie

Zhu

2021

Mathematical Problems in Engineering

Self Cite

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Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in regression problems. Regarding the feature-weighted SVR algorithms, its contribution to model output has been taken into account. However, the performance of the model is subject to the feature weights and the time consumption on training. In the paper, an efficient feature-weighted SVR is proposed. Firstly, the value constraint of each weight is obtained according to the maximal information coefficient which reveals the relationship between each input feature and output. Then, the constrained particle swarm optimization (PSO) algorithm is employed to optimize the feature weights and the hyperparameters simultaneously. Finally, the optimal weights are used to modify the kernel function. Simulation experiments were conducted on four synthetic datasets and seven real datasets by using the proposed model, classical SVR, and some state-of-the-art feature-weighted SVR models. The results show that the proposed method has the superior generalization ability within acceptable time.

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Decoupling Control of Permanent Magnet Synchronous Motor With Support Vector Regression Inverse System Method

Cited by 5 publications

References 19 publications

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

An Efficient Feature Weighting Method for Support Vector Regression

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