Related Experiment Video
Updated: Oct 20, 2025
06:42
Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
9.3K
Synchronization of Delayed Complex Networks on Time Scales via Aperiodically Intermittent Control Using Matrix-Based
IEEE Transactions on Neural Networks and Learning Systems
|September 14, 2021
Summary
This study introduces a new aperiodically intermittent control scheme for synchronizing linear complex networks with time-varying delays on time scales. The method enhances control efficiency and network synchronization, applicable to various network types.
Area of Science:
- Control Theory
- Network Synchronization
- Time Scales
Background:
- Synchronization is crucial for complex networks.
- Time-varying delays and time scales introduce significant challenges.
- Existing control methods may be inefficient.
Purpose of the Study:
- To develop an aperiodically intermittent control scheme for linear complex networks with time-varying delays on time scales.
- To achieve synchronization onto an isolated node or system.
- To enhance control efficiency and reduce resource consumption.
Main Methods:
- A matrix-based convex combination method was employed.
- A common Lyapunov function was used for special time scales.
- A special Lyapunov function with time-varying coefficients was constructed for general time scales.
- Two linear matrix inequalities were derived.
Main Results:
- A novel aperiodically intermittent control scheme was established.
- Sufficient criteria for synchronization were demonstrated.
- The proposed schemes ensure exponential convergence rate.
- Effectiveness was validated through four numerical examples.
Conclusions:
- The proposed control schemes effectively achieve synchronization for linear delayed complex networks on time scales.
- The methods are applicable to continuous-time, discrete-time, and hybrid networks.
- The approach offers reduced control consumption and communication bandwidth usage.
More Related Videos
Related Concept Videos
01:23Linear time-invariant Systems
533
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
533
01:24Fast Decoupled and DC Powerflow
358
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
358
01:21Time and frequency -Domain Interpretation of Phase-lag Control
164
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
164
01:07Time-Domain Interpretation of PD Control
206
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
Consider the example of control of motor torque. Initially, a positive...
206
01:21Linear Approximation in Time Domain
164
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
164
01:25Multimachine Stability
254
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
254