A new Gaussian Radial Basis Function Neural Network controller for induction motor control drives

The principal objective of this work consists in reducing the chattering phenomenon and ensuring more improvement of performances the proposed control scheme especially ensuring the decoupling between the control of the two principle variables of the motor “speed and flux” and also the system´s robustness towards parameter´s variations and external perturbations. In fact and as a first step a control scheme based on the combination of feedback linearization control is first proposed. Then, and in order to ensure more robustness of the process towards parameter´s variations and external perturbation…ect, sliding mode controllers are used. These controllers are based on a piecewise function for smoothing in order to reduce the effect of the chattering phenomenon which is harmful of the actuator. However, this smooth function is relied closely to the upper bound of uncertainties, which include parameter variations and external disturbances. So, fuzzy sliding mode controllers are investigated to solve this difficulty based on the method introduced by Ben-Galia et al. By replacing the gain and the signum of the attractivity function of sliding mode controllers by fuzzy maps. However, it seems that this technique couldn´t reduced the chattering considerably which let the authors to propose another type of controllers based on Gaussian Radial Basis Function Neural Network (GRBFNN) which revealed some very interesting features and prove that such controllers are efficients to attempt the required objectives.