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

460
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,...
460
Feedback control systems01:26

Feedback control systems

797
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...
797
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.9K
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...
1.9K
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

426
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,...
426
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

501
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....
501
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

999
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...
999

You might also read

Related Articles

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

Sort by
Same author

Clinical Outcomes and Complications of Surgical and Conservative Treatment for Jones Fractures: A Systematic Review.

The Journal of the American Academy of Orthopaedic Surgeons·2026
Same author

Clinical Outcomes and Safety Profile of Open and Arthroscopic Subtalar Joint Arthrodesis: A Systematic Review.

The Journal of the American Academy of Orthopaedic Surgeons·2025
Same author

Clinical Outcomes and Safety Profile for Total Ankle Arthroplasty and Ankle Arthrodesis for Symptomatic Ankle Arthritis: A Systematic Review.

The Journal of the American Academy of Orthopaedic Surgeons·2025
Same author

Clinical Outcomes and Complication Profile of Open and Arthroscopic Ankle Arthrodesis: A Systematic Review.

The Journal of the American Academy of Orthopaedic Surgeons·2025
Same author

The Safety Profile and Outcomes of Tranexamic Acid for Total Ankle Arthroplasty: A Systematic Review.

The Journal of the American Academy of Orthopaedic Surgeons·2025
Same author

Minimally invasive ultrasound-guided percutaneous plantar fasciotomy on chronic plantar fasciitis: A retrospective analysis.

Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons·2025

Related Experiment Video

Updated: Apr 26, 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

4.3K

Singularity-free neural control for the exponential trajectory tracking in multiple-input uncertain systems with

J Humberto Pérez-Cruz1, José de Jesús Rubio1, Rodrigo Encinas2

  • 1Sección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas, No. 682, Colonia Santa Catarina, México, DF 02250, Mexico.

Thescientificworldjournal
|July 22, 2014
PubMed
Summary

This study presents a novel neural network control strategy for uncertain nonlinear systems with deadzones. The method ensures accurate trajectory tracking and guarantees system stability, even with unknown dynamics.

More Related Videos

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

16.1K
Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:17

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

105

Related Experiment Videos

Last Updated: Apr 26, 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

4.3K
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

16.1K
Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:17

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

105

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Trajectory tracking in uncertain nonlinear systems is challenging due to unknown dynamics and input deadzones.
  • Existing methods often struggle with control singularities and ensuring signal boundedness.

Purpose of the Study:

  • To develop a robust control strategy for uncertain nonlinear systems with unknown symmetric deadzones.
  • To achieve precise trajectory tracking while avoiding control singularities.
  • To guarantee the boundedness of all closed-loop signals.

Main Methods:

  • Utilizing a continuous-time recurrent neural network for online identification of unknown system dynamics.
  • Developing a singularity-free feedback linearization control law based on the identified model.
  • Employing Lyapunov-like analysis to prove convergence and boundedness properties.

Main Results:

  • The proposed neural network accurately models the uncertain system dynamics.
  • The feedback linearization control law successfully achieves trajectory tracking.
  • Exponential convergence of the tracking error to a bounded zone is proven.
  • Boundedness of all closed-loop signals is guaranteed.

Conclusions:

  • The developed neural network-based control approach effectively addresses trajectory tracking for uncertain nonlinear systems with deadzones.
  • The singularity-free control design ensures reliable system performance and stability.
  • This method offers a robust solution for complex control problems in engineering applications.