Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
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.
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

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...
Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Genome-scale long noncoding RNA expression pattern in squamous cell lung cancer.

Scientific reports·2015
Same author

Short-term enhancement effect of nitrogen addition on microbial degradation and plant uptake of polybrominated diphenyl ethers (PBDEs) in contaminated mangrove soil.

Journal of hazardous materials·2015
Same author

[Structural components of Chinese medicine and pharmacology network: systematical overall regulation on pathological network].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2015
Same author

Influence of pH on hexavalent chromium reduction by Fe(II) and sulfide compounds.

Water science and technology : a journal of the International Association on Water Pollution Research·2015
Same author

Deep sequencing analysis of HBV genotype shift and correlation with antiviral efficiency during adefovir dipivoxil therapy.

PloS one·2015
Same author

An exploration of attitudes toward bystander cardiopulmonary resuscitation in university students in Tianjin, China: A survey.

International emergency nursing·2015

Related Experiment Video

Updated: Jun 17, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Generalized synchronization of complex dynamical networks via impulsive control.

Juan Chen1, Jun-An Lu, Xiaoqun Wu

  • 1School of Mathematics and Statistics, Wuhan University, Hubei 430072, China.

Chaos (Woodbury, N.Y.)
|January 12, 2010
PubMed
Summary

This study explores generalized synchronization (GS) in complex networks using impulsive control. It finds GS is achievable in nonidentical systems and influenced by network topology and control parameters.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jun 17, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Complex Systems
  • Network Science
  • Control Theory

Background:

  • Generalized synchronization (GS) is a key phenomenon in coupled dynamical systems.
  • Understanding GS in complex networks like small-world and scale-free networks is crucial for applications.
  • Impulsive control offers a method to achieve and analyze synchronization dynamics.

Purpose of the Study:

  • To investigate generalized synchronization (GS) in small-world and scale-free complex dynamical networks.
  • To analyze the impact of impulsive control strategy on achieving GS.
  • To explore the relationship between network topology and GS performance.

Main Methods:

  • Utilizing an auxiliary-system approach for theoretical analysis of GS.
  • Applying impulsive control strategies to complex dynamical networks.
  • Conducting simulations to observe GS phenomena and validate theoretical findings.

Main Results:

  • Generalized synchronization is demonstrated to occur in complex dynamical networks of nonidentical systems under impulsive control.
  • For Barabasi-Albert scale-free networks, increasing the number of edges (m) accelerates GS.
  • For Newman-Watts small-world networks, increased randomness (edge probability) speeds up GS.
  • Node dynamics and impulsive control gains significantly influence the speed and development of GS.

Conclusions:

  • Impulsive control is an effective strategy for achieving generalized synchronization in complex dynamical networks.
  • Network topology, specifically parameters like 'm' in scale-free networks and randomness in small-world networks, critically affects synchronization speed.
  • Further research into the complex interplay of node dynamics, network structure, and control parameters is warranted.