Professor : Ming – Shyan Wang Department of Electrical Engineering Southern Taiwan University Thesis progress report Sensorless Operation of PMSM Using Neural Networks Student : Sergiu Berinde M972B206

Outline  Introduction  Speed Estimation using Neural Networks  Experimental Results  To Do

Introduction  In PMSM drives, encoders or resolvers are used to get position information  These sensors increase the rotor inertia and the cost of the system  There is a desire to eliminate these sensors and many research papers deal with the subject of sensorless operation  The artificial neural networks (ANN) have found an increasing role in a wide variety of engineering applications, including power electronics and motor drive systems  Because of their ability to learn and identify nonlinear dynamics, the NNs suggest an enormous potential in motor drive systems, including sensorless operation of PMSMs  An ANN-based observer is designed to perform the rotor position and speed estimation of the PMSM

Speed Estimation using Neural Networks  Considering the electrical dynamics of the PMSM, the following model can be derived : where the state, input and output vectors are given by : - flux linkage - stator voltage - stator current - rotor angle in el. rad.

Speed Estimation using Neural Networks  The state space matrices are given by : - angular velocity in el.rad./s - flux constant - resistance - leakage inductance - self-inductance - electrical time constant

Speed Estimation using Neural Networks  Considering the above model and by doing some calculations, a relation between the angular speed, voltage and current can be obtained :  The polarity of the speed can be obtained by considering the back emf to be a space vector : where

Speed Estimation using Neural Networks  Despite the independence from the mechanical variables, this model-based approach requires some knowledge of the PMSM structure and electrical parameters  Back EMF waveshape and saliency characteristics are not always available from the manufacturer  A new approach should be considered : the neural observer  The neural observer comprises of two neural networks : speed observer and current observer

Speed Estimation using Neural Networks Fig.1 Neural network based observer

Speed Estimation using Neural Networks  The current neural network is used to estimate the stator currents  Inputs are measured currents and voltages in DQ frame and estimated speed. The observer is approximating the equations :  The output of the current observer is compared with the measured currents to yield the estimation error  The error is then backpropagated to the observer and the weights are adjusted accordingly

Speed Estimation Using Neural Networks  The speed neural network is used to estimate the angular velocity  Inputs are measured currents and voltages in αβ frame. The observer is approximating the equations  Adaptive online correction to the neural velocity observer weights is generated from the current estimation error  Since the speed changes much slower than the electrical dynamics of the current observer, the speed observer may be updated at a much slower rate

Experimental Results  To verify that the neural network speed estimation is possible, a simulation in Matlab is first conducted and some results are obtained  A TMS320F2812 DSP-drive together with a 750W 8CB75 PMSM are used to conduct experiments  Until now, only the speed observer is trained to estimate the angular velocity  The neural network used for the speed observer is a feed-forward neural network with 1 hidden layer and 5 hidden neurons  The hidden neurons use hyperbolic tangent activation functions and the output neurons use purely linear activation functions  The network is trained offline to learn the motor dynamics. The training algorithm used is Levenberg-Marquardt backpropagation

Experimental Results  Voltages, currents and velocity are measured from the motor and a training set is constructed  The training set for the speed observer consists of 211 pairs of inputs and outputs  The inputs are properly scaled and applied to the neural network. The output error is backpropagated to the previous layers and the weights are adjusted  The neural network is trained in 300 epochs

Experimental Results  Some results from simulation : Fig.2 0 rpm rpm – 600 rpm

Experimental Results  Some results from simulation : Fig.3 0 rpm rpm – 200 rpm

Experimental Results  Some results from experiment : Fig.4 0 rpm - 60 rpm

Experimental Results  Some results from experiment : Fig.5 0 rpm - 60 rpm – 195 rpm

 Train current observer  Add load to system  Check position error and plot results To Do