Chaos synchronization of coupled neurons via adaptive sliding mode control |
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| Authors: | Yan-Qiu Che Jiang Wang Shi-Gang Cui Bin Deng Xi-Le Wei Wai-Lok Chan Kai-Ming Tsang |
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| Affiliation: | 1. Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China;2. School of Electrical Engineering and Automation, Tianjin University, Tianjin, China;3. Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China |
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| Abstract: | In this paper, an adaptive neural network (NN) sliding mode controller (SMC) is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh–Nagumo (FHN) neurons under external electrical stimulation. The controller consists of a radial basis function (RBF) NN and an SMC. After the RBFNN approximating the uncertain nonlinear part of the error dynamical system, the SMC realizes the desired control property regardless of the existence of the approximation errors and external disturbances. The weights of the NN are tuned online based on the sliding mode reaching law. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as approximate error, ionic channel noise and external disturbances. Chaos synchronization is obtained by the proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method. |
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