scholarly journals Multiparameter Identification of Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System—Simulated Annealing Particle Swarm Optimization Algorithm

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 159
Author(s):  
Guoyong Su ◽  
Pengyu Wang ◽  
Yongcun Guo ◽  
Gang Cheng ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Motor Control ◽  
Simulated Annealing ◽  
Adaptive System ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Reference ◽  
Model Reference Adaptive System ◽  
Model Reference Adaptive

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

A Robust Sensorless Control of PMSM Based on Sliding Mode Observer and Model Reference Adaptive System

2017 ◽  
Vol 8 (3) ◽  
pp. 1016 ◽  
Author(s):  
Larbi M’hamed ◽  
Gherabi Zakaria ◽  
Doudar Khireddine
Keyword(s):  
Adaptive System ◽  
Sliding Mode ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Sliding Mode Observer ◽  
Model Reference ◽  
Loop Control ◽  
Model Reference Adaptive System ◽  
Loop Control System ◽  
Model Reference Adaptive

<p>This paper is intended to study and compare the operation of two methods for estimating the position/ speed of the permanent magnet synchronous motor (PMSM) under sliding mode control. The first method is a model reference adaptive system (MRAS). The second method based on sliding mode observer (SMO). The stability condition of Sliding Mode Observer was verified using the Lyapunov method to make sure that the observer is stable in converging to the sliding mode plane. In this paper the performances of the proposed two algorithms are analyzed using SIMULINK/MATLAB. The simulations results are presented to verify the proposed sensorless control algorithms and can resolve the problem of load disturbance effects by simulations which verify that the two closed-loop control system is robust with respect to torque disturbance rejection.</p>

Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control

2017 ◽  
Vol 40 (13) ◽  
pp. 3884-3898 ◽  
Author(s):  
Ridvan Demir ◽  
Murat Barut
Keyword(s):  
Motor Control ◽  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Adaptive System ◽  
Speed Sensorless ◽  
Model Reference ◽  
Model Reference Adaptive System ◽  
Model Reference Adaptive ◽  
Hybrid Estimator

This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance ([Formula: see text]) and rotor resistance ([Formula: see text]) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator currents, the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator fluxes, rotor angular velocity ([Formula: see text]), load torque ([Formula: see text]) and [Formula: see text], while the AP-MRAS provides the online [Formula: see text] estimation to the EKF. Both the AP-MRAS, whose adaptation mechanism is developed with the help of the least mean squares method in this paper, and the EKF only utilize the measured stator voltages and currents. Performances of the proposed hybrid estimator in this paper are tested by challenging scenarios generated in simulations and real-time experiments. The obtained results demonstrate the effectiveness of the introduced hybrid estimator, together with a [Formula: see text] reduction in the processing time and size of the estimation algorithm in terms of previous studies performing the same estimations of the states and parameters. From this point of view, it is the first such study in the literature, to our knowledge.

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