Jove
Visualize
Contact Us

Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

622
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
622
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

105
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
105
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

460
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
460
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

94
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
94
Feedback control systems01:26

Feedback control systems

277
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...
277

You might also read

Related Articles

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

Sort by
Same author

A data-driven subspace distributed fault detection strategy for linear heterogeneous multi-agent systems.

ISA transactionsยท2024
See all related articles
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 Experiment Video

Updated: May 31, 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

4.9K

Fully distributed data-driven model-free adaptive control for consensus tracking in multi-agent systems.

Sayed Shahab Aldin Sahafi1, Malihe Maghfoori Farsangi1

  • 1Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

ISA Transactions
|January 24, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a fully distributed model-free adaptive control (MFAC) for multi-agent systems (MASs). The novel approach uses only local information for faster consensus tracking, even with dynamic agent participation.

Keywords:
Consensus controlDistributed controlModel-free adaptive control (MFAC)Multi-agent systems (MASs)

More Related Videos

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K
Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform
07:20

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Published on: November 28, 2018

9.1K

Related Experiment Videos

Last Updated: May 31, 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

4.9K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K
Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform
07:20

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Published on: November 28, 2018

9.1K

Area of Science:

  • Robotics
  • Control Systems Engineering
  • Distributed Computing

Background:

  • Multi-agent systems (MASs) often rely on centralized or complex distributed control strategies.
  • Existing model-free adaptive control (MFAC) methods for MAS consensus tracking typically require global communication graph knowledge.
  • Dynamic changes in agent membership and communication topology pose significant challenges for MAS control.

Purpose of the Study:

  • To develop a fully distributed model-free adaptive control (MFAC) strategy for consensus tracking in multi-agent systems (MASs).
  • To enable agents to achieve consensus using only local neighbor information, eliminating the need for global communication graph knowledge.
  • To design a control method that is robust to dynamic changes in MAS topology, such as agents joining or leaving.

Main Methods:

  • Implementation of a fully distributed MFAC approach.
  • Utilization of compact form data linearization (CFDL) for controller configuration.
  • Control strategy relies solely on local information exchanged between neighboring agents.
  • Relaxation of strongly connected graph requirements, allowing for spanning tree-based consensus.

Main Results:

  • Achieved fully distributed consensus tracking in MASs.
  • Demonstrated successful control with only local information, enhancing scalability and adaptability.
  • Showcased robustness to dynamic agent addition/removal.
  • Validated faster convergence to desired trajectories compared to existing MFAC methods through simulations.

Conclusions:

  • The proposed fully distributed MFAC approach offers a scalable and robust solution for consensus tracking in MASs.
  • Local information-based control significantly simplifies implementation and enhances adaptability to network changes.
  • The method provides a faster and more efficient alternative to previous MFAC techniques for MAS consensus.
  • This work advances the field of distributed control for complex multi-agent coordination problems.