Fixed-Time Synchronization of Spatiotemporal Networks via Quantized Boundary Control

In this paper, the fixed-time synchronization of spatiotemporal networks under quantized boundary control is investigated, where the network coupling encompasses both state coupling and spatial diffusion coupling. First, under the mixed boundary condition, two innovative power-law controllers embedded with a quantization mechanism are designed, and the controllers operate at the boundary of the spatial domain. Subsequently, by employing inequality techniques and fixed-time stability theory, several verifiable criteria are derived to ensure the fixed-time synchronization of the addressed spatiotemporal networks. Lastly, the theoretical results are validated through by numerical simulations of two illustrative examples.

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Publication Details

Journal
Intelligence & Control
Published
2025-09-03
DOI
https://doi.org/10.53941/ic.2025.100003
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

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article

Fixed-Time Synchronization of Spatiotemporal Networks via Quantized Boundary Control

Tingting Shi, Cheng Hu, Juan Yu
Intelligence & Control
Neural Networks Stability and Synchronization
article

Fixed-Time Synchronization of Spatiotemporal Networks via Quantized Boundary Control

Tingting Shi, Cheng Hu, Juan Yu
article en

Abstract

In this paper, the fixed-time synchronization of spatiotemporal networks under quantized boundary control is investigated, where the network coupling encompasses both state coupling and spatial diffusion coupling. First, under the mixed boundary condition, two innovative power-law controllers embedded with a quantization mechanism are designed, and the controllers operate at the boundary of the spatial domain. Subsequently, by employing inequality techniques and fixed-time stability theory, several verifiable criteria are derived to ensure the fixed-time synchronization of the addressed spatiotemporal networks. Lastly, the theoretical results are validated through by numerical simulations of two illustrative examples.

Intelligence & ControlVol. 1(1)
Xinjiang Uygur Autonomous Region Institute of Metrology and Measurement (CN), Xinjiang University (CN)
National Natural Science Foundation of China, Xinjiang University, Natural Science Foundation of Xinjiang
Openalex Percentile: Top 29%
Neural Networks Stability and Synchronization
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Fixed-Time Synchronization of Spatiotemporal Networks via Quantized Boundary Control — Tingting Shi, Cheng Hu, et al. · Intelligence & Control (2025) | TGRS Research Map | TGRS