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Sensorless Control of the Permanent Magnet
Synchronous Motor Using Neural Networks
1,2Department of Electrical and Electronic Engineering, Fırat University 23119 Elazığ,
Turkey
Student: Chun-Yi Lin
Adviser: Ming-Shyan Wang
Date : 24th-Jun-2011
100%製作
Department of Electrical Engineering
Southern Taiwan University
Outline
Abstract
I. INTRODUCTION
II. RECURRENT BASED ROTOR POSITION ESTIMATION
A.PMSM MODEL
B. DESCRIPTION OF NEURAL BASED ROTOR ANGLE
OBSERVER
III. STRUCTURE AND TRAINING OF NEURAL-NETWORK
OBSERVERS
IV. SIMULATIONS RESULTS
V. CONCLUSIONS
References
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Abstract
In this paper, a neural network based rotor position control and speed
estimation method for Permanent Magnet Synchronous Motor (PMSM) is
proposed.
The proposed method has three recurrent neural networks. They are used for
estimating stator current, rotor speed, and rotor position angle.
Each of them is trained in two steps: off-line training for learning dynamic of
PMSM and on-line training for realizing parameter adaptation of PMSM.
Sensorless control of the permanent magnet synchronous motor using neural
networks is simulated in MATLAB/Simulink.
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I. INTRODUCTION
In permanent magnet synchronous motor (PMSM) rotor positional
sensors, which are connected to shaft, and these sensors brings some
disadvantages in PMSM applications. These disadvantages are:
• High accuracy sensors are more expensive and system costs
increase by using them.
• Accuracy of sensors is limited due to environmental factors such
as temperature, humidity, and dirt.
• Adding friction to shaft reduces ruggedness of drive and forms a
fault source.
It is desired to eliminate rotor position sensors and instead of them,
new different techniques have been developed for sensorless control.
The estimation method has three recurrent neural networks. One of
them is used to estimate rotor speed, and the other is used to estimate
stator current.
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Fig. 1. Structure of recurrent neural network
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II. RECURRENT BASED ROTOR POSITION ESTIMATION
A.PMSM MODEL:
Mathematical model of PMSM are:
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II. RECURRENT BASED ROTOR POSITION ESTIMATION
And,
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II. RECURRENT BASED ROTOR POSITION ESTIMATION
Where, λm, R, Lss, L1 and τ are permanent magnet flux
constant phase resistance, self inductance, leakage
inductance and electrical time constant of machine
respectively.
PMSM model which is transformed to rotating reference
frame can be given as in (8).
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II. RECURRENT BASED ROTOR POSITION ESTIMATION
B. DESCRIPTION OF NEURAL BASED ROTOR ANGLE OBSERVER
Neural network based sensorless control model of PMSM is shown in
Fig.2. In the block diagram, it can be seen that control model has two
artificial neural network blocks which are used to estimate rotor speed
and stator current.
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II. RECURRENT BASED ROTOR POSITION ESTIMATION
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III. STRUCTURE AND TRAINING OF NEURAL-NETWORK OBSERVERS
In case of using two hidden layer composed of 9 neurons for the neural
current observer within the system, better results were obtained in the
experiments. Also output layer consists of two neurons.
Speed observer has two hidden layers which consist of 8 and 6 neurons.
Position estimate observer includes a single hidden layer consisting of
7 neurons. Tansigmoid transfer function in the hidden layers of the
three observers within the system, and linear transfer function in the
output layers were used.
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IV. SIMULATIONS RESULTS
Sensorless simulation model of permanent magnet synchronous motor
was implemented by MATLAB 6.5 program and shown in the figure…
The model included PI controller, PWM inverter, Permanent magnet
synchronous motor, axis transformation blocks, and neural network
blocks.
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IV. SIMULATIONS RESULTS
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IV. SIMULATIONS RESULTS
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IV. SIMULATIONS RESULTS
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Conclusions
In this paper proposed neural network observer with control
system for estimating rotor position of PMSM is simulated
in MATLAB/Simulink.
By this method, it is possible to eliminate many error
calculations and submitting constant values which are
necessary in other methods.
In addition, it is possible to estimate speed and position in a
very large speed range in high accuracy by the proposed
method.
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References
[1] D. Yousfi, M. Azizi, A.Saad, “Sensorless Position and Speed Detection for Permanent
Magnet Synchronous Motor”, IPEMC 2000, Vol.3, p.1224-1229
[2] T. Senjyu, T. Shimabukuro, K. Uezato, “Position Control of Permanent Magnet
Synchronous Motors without Position and Speed Sensors” Industrial Automation and Control:
Emerging Technologies, IEEE conference, 1995, p.182-186
[3] S. Ogasawara, H. Akagi, “An Approach Position Sensorless Drive Brushless dc Motors”,
IEEE Transactions on Industry Applications, Vol.27, No.5, 1991, p.928-933
[4] M. Schroedl, “Sensorless Control of Permanent Magnet Synchronous Machines. An
Overview”, EPE-PEMC, Tagungen, Riga, 2004
[5] J. Hu, D. Zhu, B. Wu, “Permanent Magnet Synchronous Motor Drive without Mechanical
Sensors”, CCECE, IEEE, 1996, p.603-606
[6] Y. Li, L. Jiang, “Sensorless control of PMSM with an adaptive observer”, EPE’99,
Lausanne, 1999, p.1-6.
[7] G. Qingding, L. Ruifu, W. Limei, “A Shaft Sensorless Control for PMSM Using Direct
Neural Network Adaptive Observer”, Industrial Electronics, Control, and Instrumentation,
IEEE IECON 22nd International Conference ,1996, p.1729-1734
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Thank you for your attention.
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