Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Feedback control systems

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...

Linear Approximation in Time Domain

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,...

Open and closed-loop control systems

Open and closed-loop control systems

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...

Linear Approximation in Frequency Domain

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....

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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...

Control Systems

Control Systems

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...

You might also read

Related Articles

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

Sort by

Same author

Predictive value of the domain specific PLA2R antibodies for clinical remission in patients with primary membranous nephropathy: A retrospective study.

PloS one·2024

Same author

Transthoracic ultrasound localization microscopy of myocardial vasculature in patients.

Nature biomedical engineering·2024

Same author

Ultrafast 3-D Transcutaneous Super Resolution Ultrasound Using Row-Column Array Specific Coherence-Based Beamforming and Rolling Acoustic Sub-aperture Processing: In Vitro, in Rabbit and in Human Study.

Ultrasound in medicine & biology·2024

Same author

Long-term expandable mouse and human-induced nephron progenitor cells enable kidney organoid maturation and modeling of plasticity and disease.

Cell stem cell·2024

Same author

Data-Driven Dynamic Internal Model Control.

IEEE transactions on cybernetics·2024

Same author

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Water science and technology : a journal of the International Association on Water Pollution Research·2024

Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026

Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 11, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Data-Driven Internal Model Learning Control for Nonlinear Systems.

Huimin Zhang, Ronghu Chi, Biao Huang

IEEE Transactions on Neural Networks and Learning Systems

|November 16, 2023

Summary

This summary is machine-generated.

A new data-driven internal model learning control (DIMLC) strategy handles nonlinear systems with unknown uncertainties. This robust approach uses input-output data for adaptive control, enhancing system performance without an explicit model.

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Related Experiment Videos

Last Updated: Jul 11, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Area of Science:

Control Engineering
System Identification
Machine Learning

Background:

Nonlinear nonaffine systems present significant control challenges due to unknown uncertainties.
Traditional model-based control requires accurate system models, which are often unavailable or difficult to obtain.

Purpose of the Study:

To develop a novel data-driven internal model learning control (DIMLC) strategy for nonlinear nonaffine systems.
To address unknown nonrepetitive uncertainties and enhance robustness against model-plant mismatch and disturbances.

Main Methods:

Iterative dynamic linearization (IDL) to reformulate the nonlinear plant into an iterative linear data model (iLDM).
Adaptive parameter estimation of the internal model using only input-output (I/O) data.
Internal model inversion for controller design, comprising a nominal and a compensatory controller.

Main Results:

The proposed DIMLC strategy effectively controls nonlinear nonaffine systems using only I/O data.
The controller achieves perfect tracking via nominal control and offsets uncertainties with compensatory control.
Demonstrated robustness against uncertainties, model-plant mismatch, and disturbances through simulations.

Conclusions:

The data-driven DIMLC strategy offers a model-free approach for complex control problems.
This method enhances system robustness and tracking performance in the presence of uncertainties.
The validated strategy shows significant potential for practical applications in nonlinear system control.