POLITECHNIKA WARSZAWSKA

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7. Simulation and experimental results Figure 7.40. Decoupling in x-axis with estimated speed signal taken to feedback and transformation. Experimental results ( i = −0.5 → 0. 5 , time=5ms): 1) x-axis reference current, 2) x-axis current, 3) y-axis reference current, 4) y-axis current. syc 167
8. Conclusions 8. CONCLUSIONS The research results presented in this thesis based on the simulation and experimental investigations can be summarized as follows: In case of the ANN based PWM current controllers: ANN COMPARATOR • The neural network (ANN) based controllers are an interesting alternative to the conventional regulators; • The main advantages of the ANN controllers include robustness, learning ability and insensitiveness to the parameter changes; • The off-line trained multi-layers nonlinear ANN comparator has operation characteristics similar to the delta modulators. This is because the feedforward ANN cannot implement characteristic with hysteresis. Therefore, to obtain comparable accuracy of the current generation as in controller with three hysteresis comparators much higher switching frequency has to be applied. • The simple perceptron based ANN can be applied instead of the multilayer ANN. The results obtained in these two models are almost the same. OPTIMAL MODE ANN • The NN optimal regulator switches only between adjacent active vectors and zero voltage vectors. There are no ±1 transitions in the line to line voltage which reduces the stress on the inverter switching devices, • The RMS current error of the proposed optimal NN regulator is superior to the conventional Delta Regulator and the NN regulator presented in Section 4.3. • Three layers feedforward NN is able to regulate PWM inverter output current very well without on-line calculations needed in the case of the Optimal Regulator and have almost the same control quality as this optimal regulator. • The off-line NN can work faster, in the hardware implementation based on microprocessor or DSP system, compare to the Optimal Regulator. From this reason one can expect possibility of increasing the PWM frequency. 168
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POLITECHNIKA WARSZAWSKA WARSAW UNIV
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Contents 1. Introduction 4 2. Mathe
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1. Introduction 1. INTRODUCTION The
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1. Introduction In the author’s o
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3. Basics of Artificial Neural Netw
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Page 171 and 172: References REFERENCES Books and Ove
Page 173 and 174: References [34] D. Schauder, and R.
Page 175 and 176: References [66] A. Tripathi and P.
Page 177 and 178: References [97] J. W. Kolar, H. Ert
Page 179 and 180: References [129] M. P. Kazmierkowsk
Page 181 and 182: A1. Description of the simulation p
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Page 203 and 204: A3. Motor parameters A3. MOTOR PARA
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POLITECHNIKA WARSZAWSKA

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