International Journal of Control, Automation, and Systems 2024; 22(4): 1430-1441
https://doi.org/10.1007/s12555-022-0917-7
© The International Journal of Control, Automation, and Systems
The problem of H∞ exponential synchronization for switched cellular neural networks that are subjected to multiple exogenous disturbances is investigated. The exogenous disturbances are rejected and attenuated by combining disturbance observer-based control with H∞ control. Based on the admissible edge-dependent average dwell time scheme and the Lyapunov-Krasovskii functional technique, a synchronization criterion formulated by linear matrix inequalities is procured for switched cellular neural networks with external disturbances. Finally, the effectiveness of the obtained results is verified through a numerical simulation.
Keywords Admissible edge-dependent average dwell time, disturbance observer-based control, H∞ exponential synchronization, switched cellular neural networks.
International Journal of Control, Automation, and Systems 2024; 22(4): 1430-1441
Published online April 1, 2024 https://doi.org/10.1007/s12555-022-0917-7
Copyright © The International Journal of Control, Automation, and Systems.
Linlin Hou*, Pengfei Ma, Xuan Ma, and Haibin Sun
Qufu Normal University
The problem of H∞ exponential synchronization for switched cellular neural networks that are subjected to multiple exogenous disturbances is investigated. The exogenous disturbances are rejected and attenuated by combining disturbance observer-based control with H∞ control. Based on the admissible edge-dependent average dwell time scheme and the Lyapunov-Krasovskii functional technique, a synchronization criterion formulated by linear matrix inequalities is procured for switched cellular neural networks with external disturbances. Finally, the effectiveness of the obtained results is verified through a numerical simulation.
Keywords: Admissible edge-dependent average dwell time, disturbance observer-based control, H∞ exponential synchronization, switched cellular neural networks.
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