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

Feedback control systems01:26

Feedback control systems

453
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...
453
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.1K
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...
1.1K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

565
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...
565
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

278
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
278
Control Systems: Applications01:25

Control Systems: Applications

773
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...
773
Control Systems01:10

Control Systems

1.4K
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...
1.4K

You might also read

Related Articles

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

Sort by
Same author

High-Uniformity Core-Shell Nanofibers for Semiconductor Packaging: Process Optimization and Performance Study of Airflow-Assisted Coaxial Electrospinning.

Micromachines·2026
Same author

Study on the Core-Shell Structure of Gas-Assisted Coaxial Electrospinning Fibers: Implications for Semiconductor Material Design.

Micromachines·2026
Same author

Inverse Optimal Control in Conjunction With Inverse Reinforcement Learning for Distributed Parameter Systems.

IEEE transactions on cybernetics·2026
Same author

An Event-Triggered Decentralized Asynchronous Design Scheme for Positive Interconnected Switched Systems With MDMDT Switching.

IEEE transactions on cybernetics·2025
Same author

Variable Threshold-Oriented Event-Triggered Cluster Consensus for Groups of Multiagent Systems.

IEEE transactions on cybernetics·2025
Same author

Greenspace and intestinal diseases: a systematic review and meta-analysis.

BMC public health·2025

Related Experiment Video

Updated: Sep 24, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K

Event-Based Finite-Time Neural Control for Human-in-the-Loop UAV Attitude Systems.

Guohuai Lin, Hongyi Li, Choon Ki Ahn

    IEEE Transactions on Neural Networks and Learning Systems
    |May 5, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an event-based finite-time control for six-rotor unmanned aerial vehicles (UAVs). The method ensures stable consensus control despite unknown disturbances and communication load.

    More Related Videos

    Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
    07:48

    Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

    Published on: April 4, 2025

    612
    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
    07:49

    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

    Published on: November 26, 2019

    8.2K

    Related Experiment Videos

    Last Updated: Sep 24, 2025

    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
    10:51

    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

    Published on: March 10, 2011

    13.8K
    Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
    07:48

    Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

    Published on: April 4, 2025

    612
    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
    07:49

    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

    Published on: November 26, 2019

    8.2K

    Area of Science:

    • Robotics and Control Systems
    • Aerospace Engineering
    • Artificial Intelligence

    Background:

    • Consensus control is crucial for multi-agent systems like unmanned aerial vehicles (UAVs).
    • Addressing unknown disturbances and nonlinear dynamics in UAV control is challenging.
    • Finite-time control offers faster convergence compared to traditional control methods.

    Purpose of the Study:

    • To develop an event-based finite-time neural attitude consensus control strategy for six-rotor UAVs.
    • To handle unknown external disturbances and uncertain nonlinear dynamics.
    • To mitigate the communication burden in practical UAV systems.

    Main Methods:

    • Utilizing a disturbance observer to estimate and compensate for unknown external disturbances.
    • Employing radial basis function neural networks (RBF NNs) to approximate uncertain nonlinear dynamics.
    • Implementing a finite-time command filtered (FTCF) backstepping approach with an error compensation mechanism to avoid complexity explosion.
    • Integrating an event-triggered mechanism to optimize controller-actuator communication.

    Main Results:

    • The proposed control scheme ensures all signals within the six-rotor UAV systems remain bounded.
    • Consensus errors are demonstrated to converge to a small neighborhood of the origin within a finite time.
    • Simulation results validate the effectiveness and robustness of the developed control strategy.

    Conclusions:

    • The event-based finite-time neural attitude consensus control is effective for six-rotor UAVs.
    • The approach successfully addresses unknown disturbances, nonlinear dynamics, and communication constraints.
    • This research contributes to the advancement of robust and efficient UAV control systems.