Projective synchronization of different chaotic time-delayed neural networks based on integral sliding mode controller |
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| Authors: | Dong Zhang Jian Xu |
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| Institution: | 1. Department of Mathematics, Alagappa University, Karaikudi 630004, India;2. Ramanujan centre for Higher Mathematics, Alagappa University, Karaikudi 630004, India;3. School of Mathematics, Southeast University, Nanjing 211189, China;4. Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai, Thailand;5. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. School of Mathematics and Statistics, Shandong Normal University, Ji’nan\n250014, PR China;2. Department of Mathematics and Statistics, Memorial University of Newfoundland, St John’s A1C5S7, Canada;3. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Shandong Normal University, Jinan, PR China;4. School of Mathematics, Southeast University, Nanjing 210096, PR China;5. University of Carthage, Faculty of Sciences of Bizerta, Department of Mathematics, Research Units of Mathematics and Applications UR13ES47, Zarzouna, Bizerta 7021, Tunisia |
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| Abstract: | In this paper, an integral sliding mode control approach is presented to study the projective synchronization for different chaotic time-delayed neural networks. A sliding mode surface is appropriately constructed and a sliding mode controller is synthesized to guarantee the reachability of the specified sliding surface. The global asymptotic stability of the error dynamical system in the specified switching surface is investigated with the Lyapunov–Krasovskii (L–K) functional method. A delay-dependent sufficient condition is derived and the maximum time-delay value is obtained by means of the linear matrix inequality (LMI) technique. A simulation example is finally exploited to illustrate the feasibility and effectiveness of the proposed approach, verify the conservativeness of L–K method and LMI technique, and exhibit the relationship between the convergence velocity of error system and the gain matrix. |
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