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

326
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 of...
326
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.0K
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...
1.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

437
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
437
Multimachine Stability01:25

Multimachine Stability

491
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
491
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

436
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...
436
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

633
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
633

You might also read

Related Articles

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

Sort by
Same author

Observer-based quasi-bipartite consensus cloud control for multi-agent systems with stochastic communication protocol: A dynamic event-triggered approach.

ISA transactions·2026
Same author

GPIO-Based Predictive Control for Nonlinear Fully Actuated Systems Under Lumped Disturbances.

IEEE transactions on cybernetics·2026
Same author

Blockchain-Assisted Intelligent Resilient Tracking Control of Networked Systems.

IEEE transactions on cybernetics·2026
Same author

<i>Morinda citrifolia</i> L. (noni) and its potential in the management of systemic metabolic disorder (SMD).

Food & function·2025
Same author

Distributed Secondary Control for Average Voltage Recovery and Current Sharing of DC MGs via a Fully Actuated Error Model.

IEEE transactions on cybernetics·2025
Same author

Secure Tracking Control of Cyber-Physical Systems Against Hybrid Attacks via FAS Terminal Sliding-Mode Predictive Control.

IEEE transactions on cybernetics·2025
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Dec 22, 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

5.0K

Coordinated Control of Networked Multiagent Systems via Distributed Cloud Computing Using Multistep State Predictors.

Guo-Ping Liu

    IEEE Transactions on Cybernetics
    |May 5, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a distributed cloud predictive control scheme to enhance coordination in networked multiagent systems, effectively managing communication delays for improved performance and stability.

    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

    9.7K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    997

    Related Experiment Videos

    Last Updated: Dec 22, 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

    5.0K
    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

    9.7K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    997

    Area of Science:

    • Control Systems Engineering
    • Distributed Computing
    • Networked Systems

    Background:

    • Networked multiagent systems face challenges in coordinated control due to communication delays.
    • Existing control schemes struggle with scalability and computational complexity for large systems.

    Purpose of the Study:

    • To propose a distributed cloud predictive control scheme for networked multiagent systems.
    • To actively compensate for communication delays between cloud nodes and agents.
    • To ensure stability and consensus in closed-loop systems.

    Main Methods:

    • Design of a multistep state predictor for future immeasurable states.
    • Optimization of distributed cost functions for coordination control.
    • Analysis of conditions for simultaneous stability and consensus.

    Main Results:

    • The proposed scheme effectively compensates for communication delays.
    • A novel multistep state predictor enhances state estimation.
    • Coordination controller design is simplified with minimal computational overhead.
    • Conditions for system stability and consensus are derived.

    Conclusions:

    • The distributed cloud predictive control scheme offers an effective solution for coordinated control in large-scale networked multiagent systems.
    • The scheme demonstrates robustness against communication delays.
    • It provides a computationally efficient approach for achieving stability and consensus.