A Shaft Sensorless Control for PMSM Using Direct Neural Network Adaptive Observer

1
A Shaft Sensorless Control for PMSM Using Direct
Neural Network Adaptive Observer
Southern Taiwan University Department of
Electrical Engineering

• Authors Guo Qingding
• Luo Ruifu
• Wang Limei
• IEEE IECON 22nd International Conference, Vol.3,
  5-10 August 1996

Student Sergiu Berinde, M972B206
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Outline

• Abstract
• Introduction
• Multi-Layer Feedforward NN and Backpropagation
  Method
• Direct Neural Model Reference Adaptive Control
• Structure and Training of NN Observer
• Simulation Results
• Conclusions

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Abstract

• Traditional rotor position detection method is
  based on resolver, absolute encoder, etc.
• A position and velocity sensorless control
  algorithm based on direct neural model reference
  adaptive observer is proposed.
• Two neural networks are trained to learn
  electrical and mechanical model respectively,
  adaptation is realized by online training using
  current prediction error.
• Advantages of this method are shown by simulation
  results.

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Introduction

• PMSMs are highly efficient and widely used in
  servo drive applications.
• Drawbacks of using encoders or resolvers
• Expensive
• Environmental factors limit the accuracy of the
  sensor
• Additional static and dynamic friction reduce the
  ruggedness of the drive
• Some sensorless methods
• Sensing of the zero crosing of the back EMF -gt
  not very accurate
• Observer theory -gt improved approach, not well
  developed for nonlinear systems
• NN offer a promising way for the control and
  identification of systems with nonlinear
  dynamics.
• A neural network based adaptive observer is
  proposed to estimate currents, rotor velocity and
  rotor position.

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Multi-Layer Feedforward NN and Backpropagation
Method

• After initial weight and training data are given,
  the unit in the latter layer firstly receive
  input activation from preceding layer.
• Total input Xj

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Multi-Layer Feedforward NN and Backpropagation
Method

• A sigmoidal nonlinearity function is applied to
  the unit j to obtain Yj
• The activation of any node will feedforward to
  the output layer.
• When all nodes of the NN are certified, the error
  of NN can be obtained, in the form of an energy
  function
• The backpropagation learning algorithm is
  virtually an inverse process of the feedforward
  calculation.
• The output error is propagated backwards
  recursively to each lower layer and the weights
  are adjusted according to the error of each node.

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Multi-Layer Feedforward NN and Backpropagation
Method

• Learning rule for adjusting the weights
• Some steps for calculating local error
• Calculate changing rate of an output unit when
  its activation is changed.
• Calculate changing rate when the input sum of a
  node in output layer changes.
• Calculate the changing rate of preceding layer
  unit error when a unit in preceding layer is
  changed.

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Direct Neural Model Reference Adaptive Control

• Motor Model of PMSM
• The variables involved in motor dynamics are
  represented as space vectors in the stator
  reference frame and described in matrix notation.

• L - stator phase inductance
• R - stator phase resistance
• np - no. of pole pairs
• ? - rotor speed
• T - rotor position
• k - magnet constant
• H inertia
• C - Coulomb friction coeff.
• B viscous damping coeff.

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Direct Neural Model Reference Adaptive Control

• Motor Model of PMSM
• The typical control design approach is
  transforming the motor dynamics into the rotor
  frame.

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Direct Neural Model Reference Adaptive Control

• Motor Model of PMSM
• In order to implement in computer, the equations
  are put into discrete time.

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Direct Neural Model Reference Adaptive Control

• Neural Adaptive Observer
• Considering any discrete nonlinear plant, it can
  be described by
• If xk is estimated value, then the standard form
  of the observer is
• Here, and .
• As there exist some parameter uncertainty and
  condition uncertainty in motor system, the
  open-loop estimates may seriously deviate from
  the real ones gt error feedback loop should be
  added to the observer.

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Direct Neural Model Reference Adaptive Control

• Neural Adaptive Observer
• A direct neural adaptive observer is adopted to
  compensate the uncertainties.
• The two NN are trained offline to learn the
  dynamics, then the observer is trained online to
  compensate the effect of parameter variations.

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Direct Neural Model Reference Adaptive Control

• Correction of Neural Observer
• State feedback correction is important to
  maintain high precision of the estimated value.
• In this paper, the adaptive correction of ?(k) is
  accomplished by means of the output current error
  e
• Reason the electrical variable i responds faster
  to the noise than the mechanical variable ? gt
  good adaptability.
• The output error is backpropagated to the two NN
  independently and the weights are adjusted gt
  online training.

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Structure and Training of NN Observer

• Structure Selection of NN Observer
• If the structure is selected correctly, the NN
  can map any nonlinear function, given a set of
  input-output sample pairs.
• Using the discrete equations for speed and
  current, the NNs learn the electrical and
  mechanical model of the motor.
• Input vector of speed observer
  . Input of current observer
• In order to reduce the memory space and running
  times, a three layer structure of the NNs is
  used.

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Structure and Training of NN Observer

• Training of NN Observer
• The training is divided into offline training
  (learn dynamics) and online training (corrective
  procedure).
• At time step k, the input components are applied
  to the NNs and the output is compared with the
  desired response. The error is then used to
  adjust the weights.

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Structure and Training of NN Observer

• Training of NN Observer
• Learning rate is set to 0.5 and the criteria used
  to stop training is 0.003.
• Training patterns selected cover all operating
  regions including starting, acceleration and
  breaking.
• Online training is just the corrective procedure.

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Simulation Results

• A DSP TMS320C30 is used as coprocessor.
• Sampling time of adaptive observer 100us.
• Sampling time of speed controller 1ms.

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Simulation Results

• The motor under control is a 2.5kW surface
  mounted PM motor.
• For testing the adaptive capability and the
  robustness of the proposed observer, 10 noise is
  added to the measured variables.
• To ensure stability, the correction process of
  the observer is not carried out in every sample
  period.

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Simulation Results

• After starting, there is an error between
  estimated and the actual speed, but it decreases
  during stable operation.
• Although there exists error and dead time, the
  estimated speed can satisfy the requirement of
  the system.

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Simulation Results

• At a constant speed of 500rpm, the estimated
  rotor position can track the actual signal well.
• A random variation of the load torque from 0Nm to
  0.2Nm is added gt the estimated waveform contains
  a little ripple and delay.

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Conclusions

• A new sensorless method is proposed. A NN based
  observer is adopted to estimate velocity and
  rotor position.
• Some advantages compared to other methods
• Nonlinear observe ability
• Learning and adaptive ability
• Robustness to noise
• Simulations were carried out and the results show
  that the proposed method exhibits good estimating
  performance.
• The prediction errors are kept within a small
  region.