Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Saravanan Shanmugam, Rajarathinam Vadivel and Nallappan Gunasekaran ()
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Saravanan Shanmugam: Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, Tamilnadu, India
Rajarathinam Vadivel: Department of Mathematics, Faculty of Science and Technology, Phuket Rajabhat University, Phuket 83000, Thailand
Nallappan Gunasekaran: Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan

Mathematics, 2023, vol. 11, issue 10, 1-24

Abstract: In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

Keywords: Lyapunov–Krasovskii functional; event-triggered control; neural networks; synchronization; finite-time stability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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