Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Time-Domain Interpretation of PD Control

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...

Open and closed-loop control systems

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...

Propagation of Action Potentials

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...

Transfer Function in Control Systems

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...

Feedback control systems

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...

BIBO stability of continuous and discrete -time systems

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.

You might also read

Related Articles

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

Sort by

Same author

Dual pH/Redox-Responsive Mixed Polymeric Micelles for Anticancer Drug Delivery and Controlled Release.

Pharmaceutics·2019

Same author

Measurement of Dioxin Emissions from a Small-Scale Waste Incinerator in the Absence of Air Pollution Controls.

International journal of environmental research and public health·2019

Same author

Synthesis of Humin-Phenol-Formaldehyde Adhesive.

Polymers·2019

Same author

Comparison of Satellite Sign and Island Sign in Predicting Hematoma Growth and Poor Outcome in Patients with Primary Intracerebral Hemorrhage.

World neurosurgery·2019

Same author

A genetic variation in the CpG island of pseudogene GBAP1 promoter is associated with gastric cancer susceptibility.

Cancer·2019

Same author

Upregulation of FoxP4 in HCC promotes migration and invasion through regulation of EMT.

Oncology letters·2019

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

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

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

Published on: May 8, 2021

Synchronization of complex dynamical networks via impulsive control.

Gang Zhang¹, Zengrong Liu, Zhongjun Ma

¹Department of Mathematics, Shijiazhuang College, Shijiazhuang, 050035, China.

Chaos (Woodbury, N.Y.)

|January 1, 2008

Summary

This study introduces an impulsive control method for achieving synchronization in complex dynamical networks with unknown coupling. The strategy leverages node weights to enable synchronization using only a few key nodes, demonstrated with the chaotic Chua system.

More Related Videos

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

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

Published on: March 2, 2015

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Related Experiment Videos

Last Updated: Jul 8, 2026

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

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

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

Published on: March 2, 2015

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Area of Science:

Complex Systems
Control Theory
Network Science

Background:

Synchronization is crucial for complex dynamical networks.
Controlling synchronization in networks with unknown coupling is challenging.

Purpose of the Study:

To propose an impulsive control scheme for achieving synchronization in complex dynamical networks with unknown coupling.
To demonstrate that synchronization can be achieved with a few selected nodes by adjusting their weights.

Main Methods:

Utilized stability theory for impulsive differential equations.
Developed an impulsive control strategy considering node influence and weights.
Employed the chaotic Chua system for simulation.

Main Results:

Successfully achieved impulsive synchronization in complex dynamical networks.
Showcased that effective synchronization is possible by selecting and weighting a subset of nodes.
Validated the proposed impulsive control scheme's effectiveness through simulations.

Conclusions:

The proposed impulsive control scheme is effective for achieving synchronization in complex dynamical networks.
Node weighting offers a practical approach to reduce control complexity by using fewer nodes.
The method is robust and applicable to systems like the chaotic Chua network.