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

322
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...
322
Solution Equilibrium and Saturation01:59

Solution Equilibrium and Saturation

21.3K
Imagine adding a small amount of sugar to a glass of water, stirring until all the sugar has dissolved, and then adding a bit more. You can repeat this process until the sugar concentration of the solution reaches its natural limit, a limit determined primarily by the relative strengths of the solute-solute, solute-solvent, and solvent-solvent attractive forces. You can be certain that you have reached this limit because, no matter how long you stir the solution, undissolved sugar remains. The...
21.3K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

218
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
218
Feedback control systems01:26

Feedback control systems

614
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...
614
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

223
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...
223
Classification of Systems-I01:26

Classification of Systems-I

493
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
493

You might also read

Related Articles

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

Sort by
Same author

Targeting frizzled receptors (FZDs) for anti-tumor therapy: From orthosteric to allosteric inhibition.

Acta pharmaceutica Sinica. B·2026
Same author

Identification of LAMP2 as the direct target of aloperine derivatives for degrading PD-L1 through the lysosomal pathway based on deep learning model.

Bioorganic chemistry·2026
Same author

Targeting UXS1-Dependent Glucuronate Detoxification Potentiates Metformin's Anti-Tumor Efficacy in Lung Adenocarcinoma.

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

Cytokines and cancer-associated fibroblasts.

Journal of hematology & oncology·2026
Same author

Recent advances in DNA hydrogels for biomedical applications.

International journal of pharmaceutics·2026
Same author

Recognition-Based Chiral Amplification through Adaptive Locking.

Journal of the American Chemical Society·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: Dec 19, 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

5.3K

Data-Driven Terminal Iterative Learning Consensus for Nonlinear Multiagent Systems With Output Saturation.

Xuhui Bu, Jiaqi Liang, Zhongsheng Hou

    IEEE Transactions on Neural Networks and Learning Systems
    |June 5, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces novel data-driven consensus protocols for nonlinear multiagent systems (MASs) with unknown dynamics and output saturation, achieving finite-time consensus efficiently.

    More Related Videos

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.7K

    Related Experiment Videos

    Last Updated: Dec 19, 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

    5.3K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.7K

    Area of Science:

    • Control Theory
    • Systems Engineering
    • Robotics

    Background:

    • Nonlinear multiagent systems (MASs) face challenges with unknown dynamics and output saturation.
    • Achieving finite-time consensus is crucial for coordinated behavior in MASs.

    Purpose of the Study:

    • To develop data-driven consensus protocols for nonlinear MASs with unknown dynamics and output saturation.
    • To guarantee finite-time consensus independent of system dynamics.
    • To extend the protocols to systems with switching topologies.

    Main Methods:

    • Establishing a mapping relationship between terminal output and control input.
    • Utilizing terminal iterative learning control.
    • Proposing distributed, data-driven consensus protocols using saturated input/output data.

    Main Results:

    • Two novel distributed data-driven consensus protocols are proposed.
    • Convergence conditions independent of agent dynamics are developed for fixed topologies.
    • Finite-time consensus is guaranteed for two distinct objectives.
    • The design is successfully extended to switching topologies.

    Conclusions:

    • The proposed data-driven protocols effectively achieve finite-time consensus in nonlinear MASs.
    • The methods are robust to unknown dynamics and output saturation.
    • The approach is validated through simulation, demonstrating practical applicability.