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 time-invariant Systems01:23

Linear time-invariant Systems

263
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
263
Feedback control systems01:26

Feedback control systems

319
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...
319
Open and closed-loop control systems01:17

Open and closed-loop control systems

767
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...
767
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

84
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,...
84
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

403
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
403
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

492
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...
492

You might also read

Related Articles

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

Sort by
Same author

Curriculum Vitae of WEE2 Kinase in Homeostasis and Diseases: A Systematic Review.

Cells·2026
Same author

Mitochondrial genomes of Dactylogyrus wunderi (Monopisthocotyla: Dactylogyridae): structural features, codon usage patterns, and phylogenetic implications.

BMC genomics·2026
Same author

Regulation of Pore Evolution via Progressive Electroporation Enhanced Intracellular Molecule Transport.

Research (Washington, D.C.)·2026
Same author

Compression-induced metabolic adaptation drives confined tumor cell migration and distant metastasis via malate-dependent microtubule reinforcement.

Cell research·2026
Same author

ZDHHC5: a pivotal palmitoyltransferase orchestrating signaling networks - unraveling mechanisms and therapeutic horizons.

Biomarker research·2026
Same author

Harmine Targets Peroxiredoxin 6 to Enhance Macrophage Immunity Against <i>Pseudomonas plecoglossicida</i> in Ayu (<i>Plecoglossus altivelis</i>).

Antioxidants (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 11, 2025

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

SDO-Based Command Filtered Adaptive Neural Tracking Control for MIMO Nonlinear Systems With Time-Varying Constraints.

Shumin Lu, Mou Chen, Yan-Jun Liu

    IEEE Transactions on Cybernetics
    |November 8, 2023
    PubMed
    Summary

    This study introduces an adaptive neural control for nonlinear systems, using a saturation disturbance observer (SDO) to handle uncertainties and constraints effectively. The novel approach ensures stable tracking performance under complex, time-varying conditions.

    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

    4.4K
    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: Jul 11, 2025

    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
    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

    4.4K
    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
    • Nonlinear System Dynamics
    • Artificial Intelligence in Control

    Background:

    • Nonlinear systems with uncertainties and time-varying constraints pose significant control challenges.
    • Existing control methods often struggle with composite disturbances and complexity explosion.

    Purpose of the Study:

    • To develop an adaptive neural tracking control for multiple-input-multiple-output (MIMO) nonlinear systems.
    • To address system uncertainties, time-varying constraints, and composite disturbances.

    Main Methods:

    • Utilized neural networks (NNs) for approximating system uncertainties.
    • Proposed a saturation disturbance observer (SDO) to estimate composite disturbances.
    • Implemented a three-layered constraint system including prescribed performance functions (PPFs), actual, and virtual constraints.
    • Introduced a time-varying barrier Lyapunov function (TVBLF) to manage virtual constraints and avoid singularity.
    • Incorporated a command filter to mitigate the 'explosion of complexity' problem.

    Main Results:

    • The SDO demonstrated reduced estimation errors compared to traditional observers.
    • The multi-layered constraints ensured errors stayed within prescribed bounds and actual constraints were never violated.
    • The proposed control scheme effectively solved the singularity problem associated with traditional TVBLFs.
    • Numerical simulations validated the effectiveness of the control scheme, particularly for unmanned aerial vehicle flight control.

    Conclusions:

    • The developed adaptive neural tracking control scheme offers a robust solution for complex nonlinear systems.
    • The integration of SDO, NNs, and a novel constraint handling mechanism provides enhanced stability and performance.
    • The method is effective in practical applications, as shown by the UAV flight control example.