Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation
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-trigger...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/10/2257 |
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| author | Saravanan Shanmugam Rajarathinam Vadivel Nallappan Gunasekaran |
| author_facet | Saravanan Shanmugam Rajarathinam Vadivel Nallappan Gunasekaran |
| author_sort | Saravanan Shanmugam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:31:56Z |
| format | Article |
| id | doaj.art-1152407201a2479bbd30eff7acede868 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T03:31:56Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-1152407201a2479bbd30eff7acede8682023-11-18T02:18:18ZengMDPI AGMathematics2227-73902023-05-011110225710.3390/math11102257Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator SaturationSaravanan Shanmugam0Rajarathinam Vadivel1Nallappan Gunasekaran2Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, Tamilnadu, IndiaDepartment of Mathematics, Faculty of Science and Technology, Phuket Rajabhat University, Phuket 83000, ThailandComputational Intelligence Laboratory, Toyota Technological Institute, Nagoya 468-8511, JapanIn 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.https://www.mdpi.com/2227-7390/11/10/2257Lyapunov–Krasovskii functionalevent-triggered controlneural networkssynchronizationfinite-time stability |
| spellingShingle | Saravanan Shanmugam Rajarathinam Vadivel Nallappan Gunasekaran Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation Mathematics Lyapunov–Krasovskii functional event-triggered control neural networks synchronization finite-time stability |
| title | Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation |
| title_full | Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation |
| title_fullStr | Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation |
| title_full_unstemmed | Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation |
| title_short | Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation |
| title_sort | finite time synchronization of quantized markovian jump time varying delayed neural networks via an event triggered control scheme under actuator saturation |
| topic | Lyapunov–Krasovskii functional event-triggered control neural networks synchronization finite-time stability |
| url | https://www.mdpi.com/2227-7390/11/10/2257 |
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