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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

103
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...
103
Open and closed-loop control systems01:17

Open and closed-loop control systems

678
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...
678
Control Systems: Applications01:25

Control Systems: Applications

582
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
582
Control System Problem01:21

Control System Problem

110
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
110
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

490
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
490
Feedback control systems01:26

Feedback control systems

296
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...
296

You might also read

Related Articles

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

Sort by
Same author

A Pan-Methylome Framework for Population-Scale Bacterial Epigenomics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

GWO-Optimized BPNN for Abrasion Resistance Prediction of Nano-SiO<sub>2</sub> and Hybrid Fiber Reinforced Geopolymer Gel Concrete.

Gels (Basel, Switzerland)·2026
Same author

Individual and Synergistic Effects of Hybrid PVA-Steel Fiber on Mechanical Properties of Nano-SiO<sub>2</sub> Modified Epoxy Resin Gel Mortar.

Gels (Basel, Switzerland)·2026
Same author

Cost-effectiveness of statins, berberine, and combination for primary cardiovascular disease prevention in Scotland.

NPJ cardiovascular health·2026
Same author

Accuracy assessment of an artificial intelligence model in predicting intraoperative bone resection parameters for robotic-assisted total knee arthroplasty.

Scientific reports·2026
Same author

Prediction of Impact Resistance of Nano-SiO<sub>2</sub> and Hybrid Fiber Modified Geopolymer Gel Concrete in Marine Wet-Thermal and Chloride Salt Environment.

Gels (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 13, 2025

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

4.3K

Cooperative Online Learning for Multiagent System Control via Gaussian Processes With Event-Triggered Mechanism.

Xiaobing Dai, Zewen Yang, Sihua Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |September 16, 2024
    PubMed
    Summary

    This study introduces an online cooperative learning algorithm for multiagent systems (MASs) using Gaussian process (GP) regression. An event-triggered mechanism enhances data efficiency and control performance in MASs with unknown dynamics.

    More Related Videos

    Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories
    04:15

    Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

    Published on: February 23, 2024

    980
    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

    Related Experiment Videos

    Last Updated: Jun 13, 2025

    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

    4.3K
    Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories
    04:15

    Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

    Published on: February 23, 2024

    980
    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

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Multiagent systems (MASs) with unknown dynamics pose significant control challenges.
    • Gaussian process (GP) regression offers flexible nonlinear function modeling and prediction error bounds for uncertainty inference.
    • Online learning enhances GP model predictions by incorporating new data during operation.

    Purpose of the Study:

    • To investigate an online cooperative learning algorithm for multiagent systems (MASs) control.
    • To develop an event-triggered data selection mechanism for improved data efficiency in GP-based MAS control.
    • To validate the practical convergence and tracking performance of the proposed learning-based control strategy.

    Main Methods:

    • Online cooperative learning algorithm utilizing Gaussian process (GP) regression.
    • Event-triggered data selection mechanism inspired by centralized event-trigger (CET) analysis.
    • Lyapunov theory for validating system convergence and tracking performance guarantees.

    Main Results:

    • The proposed online learning algorithm ensures practical convergence of MASs.
    • Guaranteed tracking performance is achieved for the multiagent systems.
    • The event-triggered mechanism effectively reduces model update frequency and enhances data efficiency.
    • Zeno behavior is demonstrably excluded for individual agents.

    Conclusions:

    • The developed event-triggered online learning method is effective for cooperative control of MASs with unknown dynamics.
    • The approach enhances prediction accuracy and control performance through efficient data utilization.
    • Theoretical guarantees for convergence and tracking performance are provided using Lyapunov stability analysis.