| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系所 === 93 === In general, applying traditional artificial neural
network structure to control systems requires off-line
learning and training to obtain the optimal parameters.
However, it is not easy to get suitable training data
and as a result the performance of response of designed
control system is not as expected in such circumstances.
Therefore,to improve the drawbacks of on-line learning and
adaptation properties under the traditional Artificial
Neural Network architecture, this paper adopts the
projection algorithm in adaptive theory to the Neural
Network and a so-called Neural Network PI Controller(NNPIC)
is designed to construct a new Neural Network with online
learning ability, which can properly reflect dynamical
characteristics of the control system and hence provide the
adaptation ability and robustness.
Based on the projection algorithm, this paper also
proposes the Neural Network speed controller, which is
combined with the Adaptive Pseudo-Reduced-Order Flux
Observer, for the sensorless of Induction Motor Vector
Control System.Accommodating with the high power efficiency
control algorithm,the controller increases the motor
operation efficiency, and gains the advantages of superior
dynamical property and power-saving.
From the experimental results, the speed sensorless
adaptive vector control systems with the proposed
Artificial Neural Network Controller shows excellent
performance in both transient and steady-state responses.
In addition, the Adaptive Flux Observer and rotor
resistance estimator still keep the desired speed responses
and robustness within the +-20% variation range of parameters.
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