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

427
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...
427
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

125
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,...
125
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

131
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....
131
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Linear time-invariant Systems01:23

Linear time-invariant Systems

420
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...
420

You might also read

Related Articles

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

Sort by
Same journal

In-situ enhancement of autotrophic nitrogen removal in coking wastewater using staged diatomite and pyrite strategy.

Communications engineering·2026
Same journal

Thermo-mechanical behavior and thermal regulation measures of subgrade layer in roads under stochastic periodic thermal disturbance.

Communications engineering·2026
Same journal

Network architecture follows coupling in multiphysics systems: single vs. multiple branches in DeepONet and S-DeepONet.

Communications engineering·2026
Same journal

A robust GaN p-FET with unconventional electron conduction.

Communications engineering·2026
Same journal

Mobile charges in MoS<sub>2</sub>/high-k oxide transistors: from abnormal instabilities to transient negative differential resistance.

Communications engineering·2026
Same journal

Bubble-raft inspired shape-assembly in flying robot swarm for uniform formation and obstacle traversal.

Communications engineering·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 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

Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems.

Jinming Sun1, Yanqiu Huang2, Wanli Yu3

  • 1Institute of Electrodynamics and Microelectronics, University of Bremen, Bremen, Germany. jinming@uni-bremen.de.

Communications Engineering
|August 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel regulator-based Koopman operator strategy for adaptive nonlinear system modeling. It enables rapid, real-time model recalibration in dynamic environments without retraining, enhancing robustness for embedded applications.

More Related Videos

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Related Experiment Videos

Last Updated: Sep 13, 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
Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Area of Science:

  • Control Engineering
  • Applied Mathematics
  • System Dynamics

Background:

  • Adaptive modeling is crucial for real-time analysis and control of nonlinear systems facing unpredictable environmental changes.
  • Current nonlinear modeling methods struggle with online training complexity and slow recalibration.
  • Challenges include system degradations from corrosion, thermal drift, and interference.

Purpose of the Study:

  • To develop a novel strategy for real-time adaptive modeling of nonlinear systems.
  • To address limitations of existing methods in handling dynamic environmental changes.
  • To enable rapid model recalibration without offline retraining.

Main Methods:

  • A regulator is applied to the Koopman operator for adaptive modeling.
  • The regulator is implemented directly within the nonlinear state-space.
  • The approach avoids disruption of pre-trained black-box predictors.

Main Results:

  • The proposed technique effectively captures diverse nonlinear dynamics.
  • Demonstrates rapid adaptability to system variations.
  • Achieves real-time model recalibration without offline retraining.

Conclusions:

  • The regulator-based Koopman operator strategy offers a robust solution for adaptive nonlinear system modeling.
  • Its lightweight and high-speed performance are ideal for embedded systems.
  • Enables fast model recalibration and enhanced robustness in dynamic environments.