The Fuzzy PI Controller for PMSM’s Speed to Track the Standard Model

Nguyen Vu Quynh

doi:10.1155/2020/1698213

Abstract

This paper proposes a fuzzy PI controller to control the speed of a permanent magnet synchronous motor (PMSM). The structure of the system includes the speed loop controller (SLC) and the current loop controller (CLC). The speed loop controller is the fuzzy PI and standard model (SM). The CLC includes vector control and the space vector pulse width modulation (SVPWM). It compiles two closed-loop control systems for the PMSM. This research uses a very high-speed integrated circuit hardware description language (VHDL) to implement the proposed algorithm and embed it into Matlab/Simulink for simulation. Based on the PMSM parameter, this article tests the controller’s correctness with some of the load cases by changing the combined inertia and viscous friction of rotor and load. After success in simulation, the system is tested again by experiment on the FPGA kit. The simulation and experiment results show that when the load changes, the PMSM speed is still stable. The novelty of this research is that it compares two kinds of controllers between simulation and experiment results.

Highlights

Introduction epermanent magnet synchronous motor (PMSM) are commonly used in systems requiring high precision such as the robot and mechanical processing
From (36) and (37), we get the function between ωr and uf: μ 1 μB3 = 1 – μB2 μB2
E simulation results show that when using the fuzzy PI controller, the responding speed of the motor is obtained in the case of the light-load, normal-load, and heavy-load condition, and heavy-load (Figures 16–18)

Summary

Result

When the motor had a heavy load or light load, the difference between rotor speed and output of SM increased, the fuzzy adjustment mechanism (figure 1) changes the knowledge base of the fuzzy PI controller. With this method, the motor speed is still in the balance. Where dn,m Δ μAn(E) ∗μBm(dE) and cm,n are adjustable parameters in the adaptation mechanism It can be obtained by the equations (28)–(31). E purpose of the fuzzy adjustment mechanism is to create the signal to adjust the fuzzy knowledge base of the fuzzy PI controller so that the error between the speed of the rotor and the output of the SM is the smallest. From (36) and (37), we get the function between ωr and uf:

C41 C42 C43 C44 C45 C46 C47

Experiment Work