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Department of Electrical Engineering Southern Taiwan University Thesis progress report Sensorless Operation of PMSM Using Neural Networks Professor : Ming – Shyan Wang 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 : x A x Bu y Cx where the state, input and output vectors are given by : x [ x1 x2 x3 x4 ]T [ cos sin ]T , - flux linkage u [u1u2 ]T [v v ]T v , - stator voltage i , - stator current y [ y1 y2 ] [i i ] T T - rotor angle in el. rad. Speed Estimation using Neural Networks The state space matrices are given by : A 0 0 0 C R 0 3 m 2 0 0 R 0 0 0 0 e 3 m 2 R 0 3 m 2 e 0 0 0 3 m 2 R 1 0 B 0 0 e m 0 1 0 0 R 3 Ll Lss 2 - angular velocity in el.rad./s - flux constant R - resistance Ll - leakage inductance Lss - 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 : e v Ri v Ri 2 2 1 2 3 m 2 The polarity of the speed can be obtained by considering the back emf to be a space vector : e sign emf k emf k 1e where Ri k v k emf k tan v k Ri k 1 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 iˆD ,Q Inputs are measured currents and voltages in DQ frame and estimated speed. The observer is approximating the equations : iˆD ( R pLd ) vD Lq̂eiQ ˆiQ ( R pLq ) vQ Ld ˆ eiD 3 mˆ e 2 The output of the current observer is compared with the measured currents to yield the estimation error eD,Q (k ) iD,Q iˆD,Q 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 ̂e 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 - 350 rpm – 600 rpm Experimental Results Some results from simulation : Fig.3 0 rpm - 750 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 To Do Train current observer Add load to system Check position error and plot results