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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Barazane, L. Laribi, M. Krishan, M.M. Ouiguini, R. |
| Copyright Year | 2010 |
| Description | Author affiliation: Al Balq'a Applied University, Engineering of information and measurement systems, Jordan (Krishan, M.M.) || Polytechnic School of Algeria, Department of Electronic Engineering, Algiers, Algeria (Laribi, M.) || Centre of the Development of advanced Technologies, Laboratory of Robotic and artificial Intelligence, Baba Ahcen, Algier, Algeria (Ouiguini, R.) || University of the Sciences and Technology Houari Boumediene (USTHB), Faculty of Electronic and Computing, Algiers, Algeria (Barazane, L.) |
| Abstract | 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. |
| Starting Page | 545 |
| Ending Page | 550 |
| File Size | 274757 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424480913 |
| e-ISBN | 9781424480920 |
| DOI | 10.1109/MED.2010.5547726 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-23 |
| Publisher Place | Morocco |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Induction motor Induction motors feedback-linearization control Neurons sliding mode control Process control Switches Robustness Gaussian Radial Basis Function Neural Network controller Sliding mode control fuzzy sliding mode control |
| Content Type | Text |
| Resource Type | Article |
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