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

349
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...
349
Open and closed-loop control systems01:17

Open and closed-loop control systems

822
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...
822
Control Systems01:10

Control Systems

1.2K
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...
1.2K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

114
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....
114
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

442
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....
442
Linear time-invariant Systems01:23

Linear time-invariant Systems

297
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...
297

You might also read

Related Articles

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

Sort by
Same author

DNA Topoisomerase Iα Affects the Floral Transition.

Plant physiology·2016
Same author

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.

Sensors (Basel, Switzerland)·2016
Same author

Autophagy in long propriospinal neurons is activated after spinal cord injury in adult rats.

Neuroscience letters·2016
Same author

Human Lysozyme Synergistically Enhances Bactericidal Dynamics and Lowers the Resistant Mutant Prevention Concentration for Metronidazole to Helicobacter pylori by Increasing Cell Permeability.

Molecules (Basel, Switzerland)·2016
Same author

Therapeutic effect of apatinib on overall survival is mediated by prolonged progression-free survival in advanced gastric cancer patients.

Oncotarget·2016
Same author

Prevalence of hemorrhagic fever with renal syndrome in Qingdao City, China, 2010-2014.

Scientific reports·2016

Related Experiment Video

Updated: Jul 23, 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

5.0K

Valid RBFNN Adaptive Control for Nonlinear Systems With Unmatched Uncertainties.

Hao Yu, Tongwen Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |July 18, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel adaptive tracking controller using radial basis function neural networks (RBFNNs) for nonlinear systems. The method ensures stability and improves performance by iteratively refining RBFNNs, even with uncertainties.

    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

    1.7K
    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
    10:51

    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

    Published on: March 10, 2011

    13.8K

    Related Experiment Videos

    Last Updated: Jul 23, 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

    5.0K
    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

    1.7K
    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
    10:51

    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

    Published on: March 10, 2011

    13.8K

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Nonlinear plants often exhibit unmatched uncertainties, posing challenges for traditional control methods.
    • Radial basis function neural networks (RBFNNs) offer powerful approximation capabilities but require careful design for stability in adaptive control.

    Purpose of the Study:

    • To propose a valid RBFNN adaptive control strategy for nonlinear plants with unmatched uncertainties.
    • To ensure closed-loop stability and reliable approximation accuracy by keeping RBFNN arguments within compact sets.

    Main Methods:

    • A novel iterative design method is integrated with the backstepping approach.
    • Auxiliary variables and redesigned compact sets are used to ensure RBFNN validity indefinitely.
    • A closed-loop system model is formulated for stability analysis.

    Main Results:

    • The proposed iterative method extends the validity of RBFNNs to infinite time.
    • Rigorous stability proofs and practical implementation guidelines are provided.
    • A new finding reveals that excessively large RBFNNs can degrade performance in systems with unmatched uncertainties.

    Conclusions:

    • The developed adaptive tracking controller ensures closed-loop stability and enhances performance for nonlinear systems.
    • The iterative design method offers a robust approach to RBFNN adaptive control, addressing limitations of previous methods.
    • The study provides valuable insights into the practical design and limitations of RBFNNs in complex control scenarios.