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

Hamed Zeinoddini-Meymand,Salah Kamel,Baseem Khan,Qingling Wang

doi:10.1155/2022/4936167

Hamed Zeinoddini-Meymand, Salah Kamel + Show 2 more

Open Access

https://doi.org/10.1155/2022/4936167

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Abstract

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.

Highlights

In recent decades, fuzzy logic has grown dramatically in many sciences
Using the simulation in this reference, the fuzzy neural network is compared with the PID controller, which shows that the fuzzy neural networks are able to track the reference model with acceptable accuracy as well as the metamutation problem of the PID controller. ere is no neuro-fuzzy controller
First, the fuzzy neural adaptive network detects changes in motor dynamics at different operating points and with different load torques and is trained by the combined training method of least-squares and decreasing gradients. e identification method is such that for each working point, any amount of turbulence and load torque, as well as random changes of parameters, a precise local controller such as PID, LQR, and LQG is locally designed, and the input and output of this controller set is applied to an adaptive fuzzy neural network with combined training and the network covers all controllers

Summary

Introduction

Fuzzy logic has grown dramatically in many sciences. One of the reasons for the growth of fuzzy logic is that it does not require strong mathematics, and in applying fuzzy logic to different systems, a precise dynamic model of the system is not needed [1]. E identification method is such that for each working point, any amount of turbulence and load torque, as well as random changes of parameters, a precise local controller such as PID, LQR, and LQG is locally designed, and the input and output of this controller set is applied to an adaptive fuzzy neural network with combined training and the network covers all controllers. In this case, the trained network has the ability to cope with various situations and the motor speed can be controlled with high accuracy. If the membership functions are Gaussian, the output of the first layer is calculated as follows: layer 1

Fixed nodes

AC Power Source

Degree of membership

Fuzzy PID

Conclusion