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...

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...

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...

Time-Domain Interpretation of PD Control

Time-Domain Interpretation of PD Control

Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...

PD Controller: Design

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...

PI Controller: Design

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...

You might also read

Related Articles

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

Sort by

Same author

Ribonucleic acid binding protein-mediated regulation of luteinizing hormone receptor expression in granulosa cells: relationship to sterol metabolism.

Molecular endocrinology (Baltimore, Md.)·2007

Same author

Psychological stress-induced oxidative stress as a model of sub-healthy condition and the effect of TCM.

Evidence-based complementary and alternative medicine : eCAM·2007

Same author

Overexpression of OsCOIN, a putative cold inducible zinc finger protein, increased tolerance to chilling, salt and drought, and enhanced proline level in rice.

Planta·2007

Same author

Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network.

Bioinformatics (Oxford, England)·2007

Same author

[The value of long-term postoperative follow-up after curative resection of lung cancer and common problems associated with it].

Nihon Geka Gakkai zasshi·2007

Same author

Identification of a type III thioesterase reveals the function of an operon crucial for Mtb virulence.

Chemistry & biology·2007

Search research articles

Related Experiment Video

Updated: Jul 9, 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 of robust fuzzy iterative learning control for nonlinear batch processes.

Wei Zou¹, Yanxia Shen¹, Lei Wang²

¹Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi 214122, China.

Mathematical Biosciences and Engineering : MBE

|December 5, 2023

Summary

This summary is machine-generated.

A novel composite fuzzy iterative learning control (ILC) scheme stabilizes nonlinear batch processes using a 2D fuzzy model. This robust control method ensures stability and performance, validated through simulations.

Keywords:

2D $ H_\infty $ performance fuzzy iterative learning control nonlinear batch processes robust asymptotic stability uncertain T-S fuzzy 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

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Related Experiment Videos

Last Updated: Jul 9, 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

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Control Engineering
Fuzzy Systems
Nonlinear Systems

Background:

Nonlinear batch processes present control challenges due to their complex dynamics and disturbances.
Iterative learning control (ILC) is effective for repetitive tasks but requires adaptation for nonlinear systems.
Fuzzy systems offer a powerful framework for modeling and controlling nonlinear uncertainties.

Purpose of the Study:

To propose a two-dimensional (2D) composite fuzzy iterative learning control (ILC) scheme for nonlinear batch processes.
To address non-repetitive disturbances and ensure robust asymptotic stability and 2D $H_\infty$ performance.
To develop a controller design method based on linear matrix inequalities (LMIs).

Main Methods:

Representing the nonlinear batch process using a 2D uncertain Takagi-Sugeno (T-S) fuzzy model via the local-sector nonlinearity method.
Integrating feedback control with the ILC scheme under the developed fuzzy model.
Establishing sufficient conditions for stability and performance using Lyapunov functions and matrix transformations.

Main Results:

Sufficient conditions for robust asymptotic stability and 2D $H_\infty$ performance of the closed-loop fuzzy system were derived.
Controller gains were obtained by solving a set of linear matrix inequalities (LMIs).
Simulations on a three-tank system and a continuous stirred tank reactor (CSTR) demonstrated the approach's feasibility and efficiency.

Conclusions:

The proposed 2D composite fuzzy ILC scheme effectively controls nonlinear batch processes with non-repetitive disturbances.
The method guarantees robust stability and achieves desired performance levels.
The LMI-based design provides a practical approach for controller synthesis.