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

Open and closed-loop control systems01:17

Open and closed-loop control systems

740
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...
740
Control Systems01:10

Control Systems

1.1K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.1K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

103
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...
103
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

479
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...
479
Feedback control systems01:26

Feedback control systems

308
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...
308
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

395
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....
395

You might also read

Related Articles

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

Sort by
Same author

Deep Learning Predicts Imminent Tumor Progression in Advanced Pancreatic Adenocarcinoma Using Serial CT Scans During Chemotherapy.

MedComm·2026
Same author

Caregiver concerns about preschoolers' overweight/obesity and undereating and their associations with feeding practices in China.

Archives of public health = Archives belges de sante publique·2026
Same author

Anti-PD-1 blockade reverses low-intensity electric stimulation-driven pancreatic cancer progression.

Frontiers in immunology·2026
Same author

Apigenin decreases the pathogenicity of Aeromonas hydrophila infection by inhibiting aerolysin activity and interfering with quorum sensing.

Journal of applied microbiology·2026
Same author

Xanthoxylin as a quorum-sensing inhibitor of Aeromonas hydrophila with promising therapeutic effects.

Archives of microbiology·2026
Same author

A STING signaling relay from tumor cells to macrophages mediates the improved efficacy of combination chemotherapy in pancreatic cancer.

Journal of biomedical science·2026

Related Experiment Video

Updated: Jul 1, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.0K

Asymptotical tracking control for the complex network based on the dynamic topology.

Juanxia Zhao1, Yinhe Wang1, Peitao Gao2

  • 1School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, 510006, PR China.

ISA Transactions
|March 8, 2024
PubMed
Summary

A novel tracking control scheme is introduced for complex dynamic networks (CDNs). This method enables both network nodes and links to asymptotically track desired targets, optimizing network management.

Keywords:
Asymptotical tracking controlComplex dynamic networkDynamics of links

More Related Videos

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions
08:12

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

Published on: July 11, 2017

7.4K
Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles
08:31

Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles

Published on: November 15, 2019

6.2K

Related Experiment Videos

Last Updated: Jul 1, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.0K
A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions
08:12

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

Published on: July 11, 2017

7.4K
Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles
08:31

Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles

Published on: November 15, 2019

6.2K

Area of Science:

  • Control Systems Engineering
  • Network Science
  • Systems Theory

Background:

  • Complex dynamic networks (CDNs) are increasingly prevalent in various fields.
  • Effective control strategies are needed for optimizing CDN performance and management.
  • Existing methods may not adequately address the coupled dynamics of nodes and links.

Purpose of the Study:

  • To propose a novel tracking control scheme for complex dynamic networks.
  • To enable asymptotic tracking of reference targets for both network nodes and links.
  • To provide a control strategy applicable to communication transmission networks (CTNs).

Main Methods:

  • The CDN is modeled as a composite system with two mutually coupled subsystems: nodes and links.
  • Link weights are treated as state variables within the link subsystem.
  • The control scheme comprises a designed controller for nodes and a synthesized coupling term for links.

Main Results:

  • The proposed control scheme guarantees asymptotic tracking for both the node and link subsystems.
  • The controller effectively manages the coupled dynamics of the network.
  • Simulation results demonstrate the scheme's effectiveness compared to existing approaches.

Conclusions:

  • The developed tracking control scheme is effective for complex dynamic networks.
  • This approach offers a viable solution for network optimization management in CTNs.
  • The method ensures that network components achieve desired target states.