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Backstepping wavelet neural network control for indirect field-oriented induction motor drive.

Rong-Jong Wai¹, Han-Hsiang Chang

¹Department of Electrical Engineering, Yuan Ze University, Chung Li 320, Taiwan, R.O.C. rjwai@saturn.yzu.edu.tw

IEEE Transactions on Neural Networks

|September 24, 2004

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A new decoupling system and backstepping wavelet neural network (WNN) control enhance induction motor (IM) drive precision. This robust control strategy effectively manages uncertainties for stable, high-performance position tracking.

Area of Science:

Electrical Engineering
Control Systems Engineering
Artificial Intelligence

Background:

Indirect field-oriented induction motor (IM) drives require high-precision position tracking.
System uncertainties and external disturbances challenge conventional control methods, potentially compromising stability.

Purpose of the Study:

To develop a novel decoupling system and a backstepping wavelet neural network (WNN) control strategy.
To achieve high-precision position-tracking performance in indirect field-oriented IM drives despite system uncertainties.

Main Methods:

A decoupling mechanism using model reference adaptive system theory and an online inverse time-constant estimation algorithm.
A backstepping design methodology to develop a feedback control law.
A backstepping WNN control scheme combining WNN approximation and robust control to handle uncertainties.

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Main Results:

The proposed decoupling system preserves the control characteristics of the IM drive.
The backstepping WNN control scheme effectively mimics the desired feedback control law.
Numerical simulations and experimental results validate the proposed control strategy's effectiveness for periodic motion.

Conclusions:

The integrated decoupling and backstepping WNN control system significantly improves the position-tracking accuracy of indirect field-oriented IM drives.
The robust component of the WNN control ensures stable operation even with unknown system uncertainties and disturbances.