Adaptive neural network finite-time command filtered tracking control of fractional-order permanent magnet synchronous motor with input saturation

2020 ◽  
Vol 357 (18) ◽  
pp. 13707-13733
Author(s):  
Senkui Lu ◽  
Xingcheng Wang ◽  
Yanan Li
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Fractional Order ◽  
Finite Time ◽  
Tracking Control ◽  
Permanent Magnet Synchronous Motor ◽  
Input Saturation ◽  
Synchronous Motor ◽  
Adaptive Neural Network

Fractional order controllers tuning strategy for permanent magnet synchronous motor servo drive system based on genetic algorithm–wavelet neural network hybrid method

2014 ◽  
Vol 228 (17) ◽  
pp. 3218-3239 ◽  
Author(s):  
Shiqi Zheng ◽  
Xiaoqi Tang ◽  
Bao Song
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Permanent Magnet ◽  
Fractional Order ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Wavelet Neural Network ◽  
Drive System ◽  
Servo Drive ◽  
Tuning Strategy

In this paper, a novel tuning strategy for the fractional order proportional integral and fractional order [proportional integral] controllers is proposed for the permanent magnet synchronous motor servo drive system. The tuning strategy is based on a genetic algorithm–wavelet neural network hybrid method. Firstly, the initial values of the control parameters of the fractional order controllers are selected according to a new global tuning rule, which is based on the genetic algorithm and considers both the time- and frequency-domain specifications. Secondly, the wavelet neural network is utilized to update the control parameters based on the selected initial values in an online manner which improves the capability of handling parameter variations and time-varying operating conditions. Furthermore, to improve the computational efficiency, a recursive least squares algorithm, which provides information to the wavelet neural network, is used to identify the permanent magnet synchronous motor drive system. Finally, experimental results on the permanent magnet synchronous motor drive system show both of the two proposed fractional order controllers work efficiently, with improved performance comparing with their traditional counterpart.

Position control for permanent magnet synchronous motor based on neural network and terminal sliding mode control

2020 ◽  
Vol 42 (9) ◽  
pp. 1632-1640
Author(s):  
Wenwu Zhu ◽  
Dongbo Chen ◽  
Haibo Du ◽  
Xiangyu Wang
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Finite Time ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Terminal Sliding Mode

A finite-time control strategy is proposed to solve the problem of position tracking control for a permanent magnet synchronous motor servo system. It can guarantee that the motor’s desired position can be tracked in a finite time. Firstly, for the d-axis voltage, a first-order finite-time controller is designed based on the vector control principle. Then, for the q-axis voltage, based on a radial basis function (RBF) neural network, an integral high-order terminal sliding mode controller is designed. Theoretical analysis shows that under the proposed controller, the desired position can be tracked by the motor position in a finite time. Simulation results are given to show that the proposed control method has a strong anti-disturbance ability and a fast convergence performance.

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