Composite Anti-Disturbance Synchronization Control for Delayed Neural Networks Subject to Unknown Disturbances
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Composite Anti-Disturbance Synchronization Control for Delayed Neural Networks Subject to Unknown Disturbances Ting Wang1 · Linbo Chen2 · Tao Li2 · Shumin Fei3 Received: 21 April 2020 / Revised: 27 September 2020 / Accepted: 29 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper studies the master–slave synchronization in a class of delayed neural networks under external disturbance. Initially, based on an exogenous disturbance model, an effective observer is proposed to estimate the disturbance and the estimation is further utilized to design the composite controller. Then, as for the overall closed-loop error system, by choosing an augmented Lyapunov–Krasovskii functional and using some recently reported techniques, a sufficient condition is established to guarantee the desired stability and H∞ control performance. Furthermore, by combining freeweighting matrix technique with matrix transformation one, two co-design methods are obtained to ensure the existence of observer gain and controller ones in terms of linear matrix inequalities, which can possess much less conservatism. Finally, some comparisons and simulations in an example are given to illustrate our proposed methods. Keywords Neural networks · External disturbance · Master–slave synchronization · Time-varying delay · H∞ performance
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Ting Wang [email protected] Linbo Chen [email protected] Tao Li [email protected] Shumin Fei [email protected]
1
School of Information Science and Technology, Nanjing Forestry University, Nanjing 210042, People’s Republic of China
2
School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, People’s Republic of China
3
School of Automation, Southeast University, Nanjing 210096, People’s Republic of China
Circuits, Systems, and Signal Processing
1 Introduction It is well known that many engineering fields get benefit from the chaotic behaviors, such as teleoperation control, secure communication, image processing, and so on. Then in recent years, as a typical chaotic behavior, the synchronization in complex networks has attracted much attention and many elegant results have been reported [1– 3,5,6,8–10,12–17,22,26–32,34–37], in which the master–slave case was involved. The master–slave synchronization means when some conditions are satisfied, the slave system may become synchronized with the master if the master sends driving signals to the slave, which has potential applications in teleoperation control, chemical reactions, and so on. In particular, as a kind of important complex networks, neural networks (NNs) are widely utilized to tackle lots of domains including associative memory, signal processing, optimization, and pattern recognition. Then in [1–3,5,6,8–10,12–15,26– 32,34–37], the issue on synchronization in various NNs was considerably studied, in which the convergence domains and fixed-time case were involved [1,36]. Moreover, due to inherent information transmission among neurons and finite switching sp
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