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Related Concept Videos

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Multimachine Stability01:25

Multimachine Stability

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:
Linear time-invariant Systems01:23

Linear time-invariant Systems

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 calculated...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
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Related Experiment Video

Updated: May 20, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Finite-time stochastic outer synchronization between two complex dynamical networks with different topologies.

Yongzheng Sun1, Wang Li, Donghua Zhao

  • 1School of Sciences, China University of Mining and Technology, Xuzhou 221008, People's Republic of China. yzsung@gmail.com

Chaos (Woodbury, N.Y.)
|July 5, 2012
PubMed
Summary
This summary is machine-generated.

This study achieves finite-time stochastic outer synchronization for complex networks with noise. New control methods ensure synchronization quickly, even with asymmetric network structures.

Related Experiment Videos

Last Updated: May 20, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Area of Science:

  • Complex dynamical networks
  • Stochastic differential equations
  • Control theory

Background:

  • Investigating synchronization in complex networks is crucial for understanding emergent behaviors.
  • Stochastic perturbations and finite-time dynamics present significant challenges in network synchronization.

Purpose of the Study:

  • To investigate finite-time stochastic outer synchronization between two distinct complex dynamical networks.
  • To develop controllers that ensure rapid synchronization under noise perturbation.

Main Methods:

  • Utilizing finite-time stability theory for stochastic differential equations.
  • Designing suitable controllers to achieve the desired synchronization.
  • Analyzing synchronization conditions without requiring symmetric or irreducible matrices.

Main Results:

  • Sufficient conditions for finite-time stochastic outer synchronization were derived.
  • The coupling configuration matrix does not need to be symmetric or irreducible.
  • The inner coupling matrix also does not require symmetry.

Conclusions:

  • The proposed control strategies effectively achieve finite-time stochastic outer synchronization.
  • Numerical examples validate the derived conditions and demonstrate the impact of control parameters on settling time.