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 Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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, the...
Feedback control systems01:26

Feedback control systems

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...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Linear time-invariant Systems01:23

Linear time-invariant Systems

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 calculated...

You might also read

Related Articles

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

Sort by
Same author

Efficient compression of encoder-decoder models for semantic segmentation using the separation index.

Scientific reports·2025
Same author

Comparative analysis of sandstone microtomographic image segmentation using advanced convolutional neural networks with pixelwise and physical accuracy evaluation.

Scientific reports·2025
Same author

Design of thin, wideband electromagnetic absorbers with polarization and angle insensitivity using deep learning.

Scientific reports·2025
Same author

A geometric approach for accelerating neural networks designed for classification problems.

Scientific reports·2024
Same author

Forward layer-wise learning of convolutional neural networks through separation index maximizing.

Scientific reports·2024
Same author

Challenges of using artificial intelligence to detect valvular heart disease from chest radiography.

The Lancet. Digital health·2023

Related Experiment Video

Updated: May 18, 2026

Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

Robust data-driven feedback linearization using neural network based sparse identification of nonlinear dynamics.

Shahin Razani1, Mahsan Tavakoli-Kakhki1, Ahmad Kalhor2

  • 1Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.

ISA Transactions
|May 16, 2026
PubMed
Summary

This study introduces a data-driven control framework using Sparse Identification of Nonlinear Dynamics (SINDy) and feedback linearization for nonlinear systems. It enhances control accuracy and robustness, even with system uncertainties and disturbances.

Keywords:
Data-driven controlNeural networkRBF observerRobust feedback linearizationSparse identification of nonlinear 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

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

Related Experiment Videos

Last Updated: May 18, 2026

Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

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

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

Area of Science:

  • Control Engineering
  • Nonlinear System Dynamics
  • Machine Learning for Control

Background:

  • Controlling nonlinear systems with uncertainties is challenging.
  • Traditional methods often rely on accurate first-principles models, which are difficult to obtain.
  • Data-driven approaches offer a promising alternative for complex systems.

Purpose of the Study:

  • To develop a robust data-driven control framework for nonlinear systems.
  • To integrate Sparse Identification of Nonlinear Dynamics (SINDy) with feedback linearization.
  • To improve tracking accuracy and robustness in the presence of model uncertainties and disturbances.

Main Methods:

  • Utilizing SINDy to extract low-dimensional system dynamics from data.
  • Applying identified models for state feedback linearization.
  • Incorporating a neural network-based observer to estimate and compensate for unmodeled dynamics.

Main Results:

  • Demonstrated superior tracking accuracy and faster convergence on benchmark nonlinear systems (Inverted Pendulum, Flexible-Joint Robot).
  • Achieved enhanced robustness against uncertainties and disturbances with minimal computational cost.
  • The neural network observer significantly improved stability under uncertain conditions.

Conclusions:

  • Data-driven feedback linearization provides a scalable and real-time applicable solution for complex nonlinear systems.
  • The proposed framework effectively bridges model-based and data-driven control paradigms.
  • Establishes a foundation for advanced, robust data-driven control strategies.