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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

120
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,...
120
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

129
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
129
Feedback control systems01:26

Feedback control systems

404
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...
404
Open and closed-loop control systems01:17

Open and closed-loop control systems

958
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...
958
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Load-frequency control01:28

Load-frequency control

247
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
247

You might also read

Related Articles

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

Sort by
Same author

Drug-Conjugated Tam-NHC-Gold(I) Complexes Overcome <i>ESR1</i> Mutant Breast Cancer Resistance and Downregulate the RAMP3/CALCR Signaling Pathway.

Journal of medicinal chemistry·2026
Same author

Hepatitis C virus inhibits the E2F2/PI3K/AKT signaling pathway through miR-378b and leads to glycolipid metabolism disorders in the liver.

Molecular biology reports·2026
Same author

Learning Occlusion-Dynamic Invariant Representations for Multi-Object Tracking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Designer Dynamic DNA Nanoaggregate in Living Cell for Mitochondrial Energy Restriction.

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

Discovery of NTQ2494, a potent and orally bioavailable inhibitor of AXL kinase for the treatment of human tumors.

European journal of medicinal chemistry·2026
Same author

Data-Driven Interrogation of Reactivity in Acid-Catalyzed Carbonyl-Olefin Metathesis with Machine Learning and Large Language Models.

Journal of the American Chemical Society·2026

Related Experiment Video

Updated: Sep 2, 2025

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

1.8K

Cooperative ETM-Based Adaptive Neural Network Tracking Control for Nonlinear Pure-Feedback MASs: A Special-Shaped

Qiangqiang Zhu, Ben Niu, Ding Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 1, 2022
    PubMed
    Summary

    This study presents a new cooperative adaptive tracking control for nonlinear pure-feedback multi-agent systems (MASs). It uses radial basis function neural networks and an event-triggered mechanism for resource efficiency and accurate control.

    More Related Videos

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    8.7K
    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

    5.0K

    Related Experiment Videos

    Last Updated: Sep 2, 2025

    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

    1.8K
    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    8.7K
    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

    5.0K

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Nonlinear pure-feedback multi-agent systems (MASs) present challenges in cooperative adaptive tracking control.
    • Existing methods may struggle with non-existent partial derivatives of nonaffine functions.

    Purpose of the Study:

    • To develop a novel cooperative adaptive tracking control strategy for nonlinear pure-feedback MASs.
    • To address limitations in existing adaptive control techniques for these systems.

    Main Methods:

    • Utilized backstepping technique with radial basis function neural networks (RBF NNs) to handle additional state variables.
    • Introduced a novel, specially-shaped Laplacian matrix for unifying leader gain.
    • Integrated an event-triggered mechanism (ETM) for resource optimization.

    Main Results:

    • The proposed controller effectively stabilizes system states in nonlinear pure-feedback MASs.
    • Achieved accurate tracking error convergence under the event-triggered control.
    • Demonstrated the feasibility and effectiveness of the developed control method via simulations.

    Conclusions:

    • The novel control strategy enhances cooperative adaptive tracking in MASs.
    • The integration of RBF NNs and ETM offers a robust and resource-efficient solution.
    • The proposed method overcomes limitations of prior adaptive control techniques for pure-feedback systems.