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

Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

434
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
434
Feedback control systems01:26

Feedback control systems

789
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...
789
PD Controller: Design01:26

PD Controller: Design

703
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,...
703
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

394
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
394
Control System Problem01:21

Control System Problem

483
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...
483
PI Controller: Design01:24

PI Controller: Design

1.4K
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Event-triggered fuzzy logic control for an uncertain robot with coupled output constraints.

ISA transactions·2026
Same author

WormSORT: A detection-based multiple object tracking model for individual silkworms in breeding environments.

PLoS computational biology·2026
Same author

Window-to-window BEV representation learning for limited FoV cross-view geo-localization.

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

ImagineNav++: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Research progress on chemical metabolites, processing technologies, and pharmacological activities of asperosaponin VI: a systematic review and critical evaluation.

Frontiers in pharmacology·2026
Same author

Nash Equilibrium Strategies for Multicluster Pursuit-Evasion Game With Disturbances: A Prescribed-Time Convergence Approach.

IEEE transactions on cybernetics·2026
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: Mar 22, 2026

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

5.5K

Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

Lu Dong, Xiangnan Zhong, Changyin Sun

    IEEE Transactions on Neural Networks and Learning Systems
    |April 13, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an adaptive event-triggered control method using heuristic dynamic programming (HDP) for nonlinear systems. This approach reduces computational costs by updating control laws only when necessary, ensuring system stability.

    More Related Videos

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    2.2K

    Related Experiment Videos

    Last Updated: Mar 22, 2026

    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

    5.5K
    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    2.2K

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Traditional adaptive dynamic programming (ADP) control methods involve periodic updates, leading to significant computational and transmission costs.
    • Nonlinear discrete-time systems with unknown dynamics pose challenges for precise control and stability analysis.

    Purpose of the Study:

    • To design a novel adaptive event-triggered control method for nonlinear discrete-time systems with unknown dynamics.
    • To reduce computational and transmission costs compared to traditional ADP methods.
    • To ensure asymptotic stabilization of the discrete-time systems.

    Main Methods:

    • Utilizing heuristic dynamic programming (HDP) for adaptive control.
    • Implementing an actor-critic framework to learn optimal control laws and value functions.
    • Developing a new event-triggering condition and a model network for state estimation.

    Main Results:

    • The proposed event-triggered control law is updated only upon violation of the triggering condition.
    • A Lyapunov stability analysis confirms the asymptotic stabilization of the discrete-time systems.
    • Simulation results on two distinct discrete-time systems validate the effectiveness of the proposed method.

    Conclusions:

    • The novel adaptive event-triggered control method effectively stabilizes nonlinear discrete-time systems.
    • The heuristic dynamic programming-based approach significantly reduces computational and transmission overhead.
    • The developed event-triggering mechanism is a key contribution for discrete-time systems.