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

First Order Systems01:21

First Order Systems

90
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
90
Second Order systems II01:18

Second Order systems II

109
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
109
Second Order systems I01:20

Second Order systems I

157
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
157
Linear time-invariant Systems01:23

Linear time-invariant Systems

258
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...
258
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

89
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....
89
Feedback control systems01:26

Feedback control systems

308
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...
308

You might also read

Related Articles

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

Sort by
Same author

A New Classification of Buccal Mucosa Squamous Cell Carcinoma and the Corresponding Surgical Strategy.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons·2025
Same author

[Application of sliding vermilion flap in angulus oris defect after buccal mucosa cancer ablation].

Shanghai kou qiang yi xue = Shanghai journal of stomatology·2025
Same author

Necessity of applying anatomical unit resection surgery in suspected posterior oral squamous cell carcinoma.

BMC oral health·2025
Same author

The role of unconventional lymph node metastasis in neck recurrence among patients with tongue cancer.

Clinical oral investigations·2023
Same author

LMI-Based Delayed Output Feedback Controller Design for a Class of Fractional-Order Neutral-Type Delay Systems Using Guaranteed Cost Control Approach.

Entropy (Basel, Switzerland)·2023
Same author

Disturbance observer-based delayed robust feedback control design for a class of uncertain variable fractional-order systems: Order-dependent and delay-dependent stability.

ISA transactions·2023

Related Experiment Video

Updated: Jul 1, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

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

Published on: August 15, 2020

5.0K

Precise tracking control via iterative learning for one-sided Lipschitz Caputo fractional-order systems.

Hanjiang Wu1, Jie Huang1, Kehan Wu1

  • 1Anhui Electrical Engineering Professional Technique College, Hefei 230051, China.

Mathematical Biosciences and Engineering : MBE
|March 8, 2024
PubMed
Summary

This study introduces iterative learning control (ILC) for fractional-order systems with specific nonlinearities. The proposed P-type ILC algorithms ensure perfect trajectory tracking, demonstrating effectiveness through simulations.

Keywords:
P-type learning algorithmfractional-order systemsiterative learning controlone-sided Lipschitz nonlinearity

More Related Videos

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.4K
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

Related Experiment Videos

Last Updated: Jul 1, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

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

Published on: August 15, 2020

5.0K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.4K
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

Area of Science:

  • Control Theory
  • Fractional Calculus
  • Nonlinear Systems

Background:

  • Fractional-order systems are increasingly used in modeling complex dynamics.
  • Iterative learning control (ILC) is effective for repetitive tasks.
  • One-sided Lipschitz nonlinearity presents challenges in control design.

Purpose of the Study:

  • To develop and analyze iterative learning control algorithms for Caputo fractional-order systems.
  • To address systems with one-sided Lipschitz nonlinearity.
  • To achieve perfect tracking of desired trajectories.

Main Methods:

  • Design of open- and closed-loop P-type learning algorithms.
  • Establishment of convergence conditions for the proposed algorithms.
  • Mathematical analysis of output tracking error convergence.

Main Results:

  • The proposed P-type ILC algorithms ensure output tracking error converges to zero.
  • Convergence is demonstrated along the iteration axis.
  • Theoretical findings are validated through a simulation example.

Conclusions:

  • The developed iterative learning control approach is effective for fractional-order systems with one-sided Lipschitz nonlinearity.
  • The proposed algorithms guarantee precise trajectory tracking.
  • The study provides a robust method for controlling such complex systems.