Accurate Wavelet Neural Network for Efficient Controlling of an Active Magnetic Bearing System
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Published On: 9 November 2010
Copyright: © 2020 Youssef Harkouss, Souhad Mcheik and Roger Achkar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN) approach for efficient controlling of an Active Magnetic Bearing (AMB) system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited in deriving a good initialization for the neural network and thus improved convergence of the learning algorithm. Results: We tested two control systems based on three types of neural controllers: Multiplayer Perceptron (MLP) controller, RBF Neural Network (RBFNN) controller and WNN controller. The simulation results show that the proposed WNN controller provides better performance comparing with standard PID controller, MLP and RBFNN controllers. Conclusion: The proposed WNN approach was shown to be useful in controlling nonlinear dynamic mechanical system, such as the AMB system used in this study.
- Active magnetic bearing system
- rotor unbalance
- wavelet neural network
- radial basis function
- LM algorithm
- hidden neurons
- nonlinear dynamic