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

Open and closed-loop control systems01:17

Open and closed-loop control systems

634
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...
634
Feedback control systems01:26

Feedback control systems

281
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...
281
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

62
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,...
62
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

460
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...
460
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

625
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
625
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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....
85

You might also read

Related Articles

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

Sort by
Same journal

Prescribed-time event-triggered resilient containment control for multiagent systems against DoS attacks and disturbances.

ISA transactions·2026
Same journal

Incremental learning with prototype calibration and dynamic proxy for wind turbine fault diagnosis under time-varying operating conditions.

ISA transactions·2026
Same journal

Optimization of mode discerning control for nonlinear hybrid systems subject to unknown inputs with applications to active fault diagnosis.

ISA transactions·2026
Same journal

Convergence evaluation of optimization-based stochastic iterative learning control.

ISA transactions·2026
Same journal

Adaptive utility-aware event-triggered reinforcement learning for hybrid attack scheduling against remote state estimation.

ISA transactions·2026
Same journal

Hybrid vehicle state estimation using closed-form liquid neural networks and nonlinear Kalman filtering.

ISA transactions·2026

Related Experiment Video

Updated: Jun 4, 2025

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

8.6K

Frequency-domain-based nonlinear normalized iterative learning control for three-dimensional ball screw drive

Fu Wen-Yuan1

  • 1College of Information Science and Engineering, Huaqiao University, Xiamen, 361002, China.

ISA Transactions
|January 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-driven iterative learning control (ILC) method that improves tracking performance for unknown systems without needing a plant model. The approach enhances transient performance and reduces computational load.

Keywords:
Data-basedFrequency domainIteration-varyingIterative learning controlNonrepetitive

More Related Videos

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

1.6K
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

13.7K

Related Experiment Videos

Last Updated: Jun 4, 2025

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

8.6K
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

1.6K
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

13.7K

Area of Science:

  • Control Systems Engineering
  • Robotics
  • Machine Learning

Background:

  • Iterative learning control (ILC) excels in repetitive tasks but typically requires a nominal plant model, leading to performance degradation due to model mismatches.
  • Existing ILC methods are often limited by their reliance on system identification, hindering their application in scenarios with unknown or changing dynamics.

Purpose of the Study:

  • To develop a novel, model-free normalization concept for iterative learning control (ILC) applicable to unknown discrete-time systems.
  • To enhance the transient tracking performance of ILC for both iteration-invariant and iteration-varying trajectory tracking tasks.
  • To reduce computational complexity and prevent excessive input amplification in ILC systems.

Main Methods:

  • A data-driven, frequency-domain approach is employed to develop a normalization concept, eliminating the need for explicit system model information.
  • The proposed method normalizes the input-output ratio to manage system dynamics effectively.
  • The approach is validated through experimental application on a three-dimensional ball screw drive system.

Main Results:

  • The novel normalization concept successfully enables model-free ILC for unknown discrete-time systems.
  • Significant improvements in transient tracking performance were observed compared to traditional model-based ILC methods.
  • The method demonstrated effectiveness for both iteration-invariant and iteration-varying trajectory tracking, showcasing its versatility.
  • Reduced computational complexity and controlled input amplification were achieved.

Conclusions:

  • The proposed data-driven, model-free ILC approach offers a robust solution for precise tracking in unknown systems, overcoming limitations of traditional methods.
  • This normalization technique enhances control performance and efficiency, particularly in complex, real-world applications like robotic systems.
  • The findings open new avenues for applying ILC to a broader range of dynamic systems where accurate modeling is challenging or impossible.