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Updated: Sep 23, 2025

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
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Dynamic Self-Triggered Impulsive Synchronization of Complex Networks With Mismatched Parameters and Distributed
IEEE Transactions on Cybernetics
|May 13, 2022
Summary
This study introduces a dynamic self-triggered impulsive controller for quasisynchronization in complex networks with time-varying delays. The controller adapts parameters and reduces control costs while ensuring synchronization effectiveness.
Area of Science:
- Complex network theory
- Nonlinear dynamics
- Control theory
Background:
- Synchronization in complex networks is crucial but challenging due to nonlinear couplings and time-varying delays.
- Mismatched parameters in individual systems complicate achieving synchronization.
Purpose of the Study:
- To investigate leader-following quasisynchronization in complex networks with nonlinear couplings and distributed time-varying delays.
- To develop a dynamic self-triggered impulsive controller that optimizes control intervals and reduces costs.
- To adapt system parameters for effective quasisynchronization.
Main Methods:
- Impulsive control strategies
- Dynamic self-triggered impulsive controller design
- Lyapunov stability theorem
- Comparison method
- Average impulsive interval and gain definitions
Main Results:
- Sufficient conditions for synchronization within a specific bound are derived using Lyapunov stability and average impulsive interval.
- The dynamic parameter updating laws adapt to quasisynchronization based on error bounds.
- The parameter variation scheme is extended to time-varying impulsive effects.
Conclusions:
- The proposed dynamic self-triggered impulsive controller effectively achieves quasisynchronization in complex networks with time-varying delays.
- The method reduces control costs by optimizing impulsive input instants.
- Numerical examples validate the effectiveness and superiority of the proposed approach.
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