1Automation College, Nanjing University of Aeronautics and Astronautics, Nanjing 2100162Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK
Synchronization Scheme for Uncertain Chaotic Systems via RBF Neural Network
1Automation College, Nanjing University of Aeronautics and Astronautics, Nanjing 2100162Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK
摘要A sliding mode adaptive synchronization controller is presented with a neural network of radial basis function (RBF) for two chaotic systems. The uncertainty of the synchronization error system is approximated by the RBF neural network. The synchronization controller is given based on the output of the RBF neural network. The proposed controller can make the synchronization error convergent to zero in 5s and can overcome disruption of the uncertainty of the system and the exterior disturbance. Finally, an example is given to illustrate the effectiveness of the proposed synchronization control method.
Abstract：A sliding mode adaptive synchronization controller is presented with a neural network of radial basis function (RBF) for two chaotic systems. The uncertainty of the synchronization error system is approximated by the RBF neural network. The synchronization controller is given based on the output of the RBF neural network. The proposed controller can make the synchronization error convergent to zero in 5s and can overcome disruption of the uncertainty of the system and the exterior disturbance. Finally, an example is given to illustrate the effectiveness of the proposed synchronization control method.
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2007, Vol. 24 
