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

473
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...
473
PD Controller: Design01:26

PD Controller: Design

377
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
377
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.1K
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...
1.1K
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

194
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
194
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

568
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...
568

You might also read

Related Articles

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

Sort by
Same author

Transcranial direct current stimulation improves cerebral ischemiareperfusion injury by regulating microglial ferroptosis and M1 polarization via KLF4/xCT.

Pathology, research and practice·2026
Same author

Feedback controlled adaptive time-stepping for energy preserving variational integration.

ISA transactions·2026
Same author

Case Report: Pathological confirmation and aggressive postoperative recurrence of WHO grade III rhabdoid meningioma.

Frontiers in medicine·2026
Same author

TaCML49-B, a Calmodulin-like Protein, Interacts with TaIQD23 to Positively Regulate Salt Tolerance in Wheat.

Plants (Basel, Switzerland)·2025
Same author

Functional Identification Reveals That TaTGA16-2D Promotes Drought and Heat Tolerance.

Plants (Basel, Switzerland)·2025
Same author

Identification of the Q-type ZFP gene family in Triticeaes and drought stress expression analysis in common wheat.

Genetica·2025

Related Experiment Video

Updated: Oct 6, 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

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System.

Zhijun Fu1, Yan Lu1, Fang Zhou1

  • 1Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China.

Computational Intelligence and Neuroscience
|January 17, 2022
PubMed
Summary

This study introduces a novel parametric neural network (PNN) for adaptive nonlinear system identification and trajectory tracking. The approach ensures accurate system dynamics identification and robust control performance for model-free systems.

More Related Videos

Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

9.7K
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

8.8K

Related Experiment Videos

Last Updated: Oct 6, 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
Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

9.7K
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

8.8K

Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Nonlinear System Dynamics

Background:

  • Model-free nonlinear systems present significant challenges for accurate identification and control.
  • Existing methods often struggle with unknown dynamics and modeling uncertainties.
  • Trajectory tracking in complex systems requires robust adaptive control strategies.

Purpose of the Study:

  • To develop an effective parametric neural network (PNN) for identifying unknown dynamics in model-free nonlinear systems.
  • To design an adaptive tracking controller that compensates for identified nonlinearities and modeling errors.
  • To ensure the stability and convergence of the closed-loop system using Lyapunov stability theory.

Main Methods:

  • A novel parametric neural network (PNN) identifier with a parameter error-driven updating law for accurate and rapid system identification.
  • An adaptive tracking controller combining feedback control for nonlinearity compensation and sliding mode control for modeling error management.
  • Lyapunov stability analysis to guarantee the convergence of the integrated PNN identifier and adaptive controller.

Main Results:

  • The proposed PNN identifier demonstrates high accuracy and speed in capturing unknown system dynamics.
  • The adaptive tracking controller effectively compensates for system nonlinearities and uncertainties.
  • Simulation results on an Automotive Front-End/Direct Yaw Control (AFS/DYC) system validate the approach's effectiveness.

Conclusions:

  • The developed PNN-based adaptive control strategy offers a robust solution for trajectory tracking in model-free nonlinear systems.
  • The parameter error-driven updating law enhances identification performance.
  • The combined control approach ensures system stability and reliable tracking capabilities.