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POLITECHNIKA WARSZAWSKA

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5. Speed estimation of induction motor<br />

u s<br />

Reference<br />

Model<br />

x<br />

i s<br />

i s<br />

Learning<br />

ε<br />

ANN<br />

∧<br />

x<br />

ω^m<br />

Algorithm<br />

Fig.5.8. Block scheme of an on-line ANN based speed estimator<br />

As Adaptive Model the one layer linear ANN is used. More detailed description of<br />

this approach is shown in the next section.<br />

In the off-line approach a three-layer feedforward ANN is used, and the<br />

mechanical speed is the output of the neural network. The input vector to the ANN<br />

could be as follows:<br />

xin<br />

= [ u<br />

sα<br />

(<br />

i<br />

k)<br />

sα<br />

(<br />

k)<br />

u<br />

sβ<br />

(<br />

i<br />

k)<br />

sβ<br />

(<br />

k)<br />

u<br />

sα<br />

(<br />

i<br />

sα<br />

(<br />

k −1)<br />

k −1)<br />

u<br />

i<br />

sβ<br />

(<br />

sβ<br />

(<br />

k −1)<br />

k −<br />

T<br />

1)]<br />

(5.15)<br />

The block diagram of this approach is presented in Fig. 5.9.<br />

Because of the its simplicity, and the advantages of an ANN in the further part<br />

of this thesis only approaches based on ANN are detailed described and used in real<br />

system.<br />

111

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