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

556
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...
556
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

257
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...
257
Controller Configurations01:22

Controller Configurations

222
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
222
PD Controller: Design01:26

PD Controller: Design

455
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
455
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.3K
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...
1.3K
Control Systems01:10

Control Systems

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

SACI framework-based fixed-time learning control for nonlinear systems with asymmetric constraints.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Distributed Inertial k-Winners-Take-All Neural Network Based on Quadratic Optimization Problems.

IEEE transactions on neural networks and learning systems·2026
Same author

Influence factors and model selection for conflict risk in different car-following behaviors: Insights from automated and human-driven vehicles.

Accident; analysis and prevention·2025
Same author

Nesterov Accelerated Gradient Tracking With Adam for Distributed Online Optimization.

IEEE transactions on neural networks and learning systems·2025
Same author

Observer-Based Event-Triggered Fault-Tolerant Synchronization for Memristive Neural Networks Subject to Multiple Failures.

IEEE transactions on neural networks and learning systems·2025
Same author

Finite time dynamic analysis of memristor-based fuzzy NNs with inertial term: Nonreduced-order approach.

Neural networks : the official journal of the International Neural Network Society·2025
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Nov 16, 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

5.2K

Adaptive Learning and Sampled-Control for Nonlinear Game Systems Using Dynamic Event-Triggering Strategy.

Chaoxu Mu, Ke Wang, Zhen Ni

    IEEE Transactions on Neural Networks and Learning Systems
    |February 23, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces dynamic event-triggering for adaptive dynamic programming, improving upon static rules. The new method ensures system stability and reduces communication load in control systems.

    More Related Videos

    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

    4.9K
    Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
    13:40

    Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

    Published on: December 16, 2010

    17.0K

    Related Experiment Videos

    Last Updated: Nov 16, 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

    5.2K
    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

    4.9K
    Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
    13:40

    Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

    Published on: December 16, 2010

    17.0K

    Area of Science:

    • Control Systems Engineering
    • Adaptive Dynamic Programming
    • Game Theory

    Background:

    • Static event-triggering rules in adaptive dynamic programming lack memory.
    • Existing methods do not consider previous states, limiting performance.
    • There is a need for improved triggering mechanisms in control systems.

    Purpose of the Study:

    • To develop a dynamic event-triggering strategy for nonzero-sum differential game systems.
    • To guarantee asymptotic stability and accurate approximation of critic neural networks.
    • To reduce communication burden in control loops.

    Main Methods:

    • Deduction of a static triggering rule from Hamilton-Jacobi equations.
    • Incorporation of an exponential term to ensure Zeno-free behavior.
    • Design of a novel dynamic-triggering rule using a first-order filter and a dynamic variable.

    Main Results:

    • Mathematical proofs confirm system stability and weight convergence.
    • Theoretical analysis elucidates the dynamic rule's properties and relation to static rules.
    • Numerical simulations validate the proposed dynamic event-triggering strategy.

    Conclusions:

    • Dynamic event-triggering enhances control system stability and neural network approximation.
    • The proposed dynamic strategy significantly reduces communication load compared to static methods.
    • This approach offers a more efficient solution for event-sampled nonzero-sum differential game systems.