An Adaptive Nonlinear Extended State Observer for the Sensorless Speed Control of a PMSM

Chenyi Wang,Minggang Gan

doi:10.1155/2015/807615

Abstract

This paper presents a sensorless speed control strategy for a permanent-magnet synchronous motor (PMSM) based on an adaptive nonlinear extended state observer (ANLESO). In this paper, an extended state observer (ESO), which takes back-EMF (back electromotive force) as an extended state, is used to estimate the rotor position and the rotor speed because of its simpler structure and higher accuracy. Both linear ESO (LESO) and nonlinear ESO (NLESO) are considered to estimate the back-EMF of PMSM, and NLESO is finally implemented due to its obvious advantage in convergence. The convergence characteristics of the estimation error of the observer are analyzed by the Lyapunov theory. In order to take both stability and steady-state error into consideration, an adaptive NLESO is proposed, which adaptively adjusts the parameters of NLESO to a compromised value. The performance of the proposed method was demonstrated by simulations and experiments.

Highlights

In recent years, permanent-magnet synchronous motors have increasingly gained lots of applications due to their high efficiency, high dynamic response, and high torque to current ratio [1, 2]
This paper presents a sensorless speed control strategy for a permanent-magnet synchronous motor (PMSM) based on an adaptive nonlinear extended state observer (ANLESO)
In order to show the high-speed performance of the proposed ANLESO, it is necessary to compare it with the conventional sliding mode observer (SMO) through the MatLab/Simulink programming environment

Summary

Introduction

Permanent-magnet synchronous motors have increasingly gained lots of applications due to their high efficiency, high dynamic response, and high torque to current ratio [1, 2]. By means of injecting high frequency signal into motor, the impedance difference caused by magnetic saturation, which contains the information about the rotor position, can be calculated [5]. Neural network technology has a good advantage in terms of parameter identification and it has been used in sensorless control system by many scholars [9] In these papers, the rotor position is identified online by neural network. Zhang and Li proposed the model reference adaptive scheme (MRAS) to implement a sensorless control system. The information of the rotor position and the rotor speed of PMSM can be calculated by estimating the back-EMF. BackEMF contains the information of the rotor position and speed that cannot be obtained without sensor For this reason, backEMF cannot be modeled directly.

Mathematical Model

Proposed Adaptive Nonlinear ESO

Simulation and Experimental Results

Conclusions