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

361
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...
361
PD Controller: Design01:26

PD Controller: Design

306
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,...
306
Open and closed-loop control systems01:17

Open and closed-loop control systems

856
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...
856
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

111
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,...
111
Multi-Step Reactions02:31

Multi-Step Reactions

7.4K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
7.4K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

101
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
101

You might also read

Related Articles

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

Sort by
Same author

MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model.

Life (Basel, Switzerland)·2026
Same author

[Experience in Implementing an Integrated Cardiometabolic Practice Unit in a Private Healthcare Provider: A Value-Based Care Model].

Revista medica de Chile·2025
Same author

A Hybrid Control Framework for Chemical Processes with Long Time Delay: Theory and Experiments.

ACS omega·2024
Same author

Hybrid Controller Based on Numerical Methods for Chemical Processes with a Long Time Delay.

ACS omega·2023
Same author

Psychological Adjustment Profiles of LGBTQ+ Young Adults Residing with Their Parents during the COVID-19 Pandemic: An International Study.

International journal of environmental research and public health·2023
Same author

Hybrid Approaches-Based Sliding-Mode Control for pH Process Control.

ACS omega·2022

Related Experiment Video

Updated: Aug 6, 2025

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.2K

Dual-Mode Based Sliding Mode Control Approach for Nonlinear Chemical Processes.

Camila Obando1, Ruben Rojas2, Francisco Ulloa1

  • 1Dipartimento di Informatica, Modellistica, Elettronica e Sistemistica, Università della Calabria, 87036 Rende, Italy.

ACS Omega
|March 20, 2023
PubMed
Summary

A novel dual-mode sliding mode controller (SMC) improves system performance by adjusting gain based on error size. This approach enhances tracking and regulation for nonlinear processes like CSTR and mixing tanks.

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

2.5K
Microfluidic Pneumatic Cages: A Novel Approach for In-chip Crystal Trapping, Manipulation and Controlled Chemical Treatment
09:34

Microfluidic Pneumatic Cages: A Novel Approach for In-chip Crystal Trapping, Manipulation and Controlled Chemical Treatment

Published on: July 12, 2016

9.5K

Related Experiment Videos

Last Updated: Aug 6, 2025

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.2K
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

2.5K
Microfluidic Pneumatic Cages: A Novel Approach for In-chip Crystal Trapping, Manipulation and Controlled Chemical Treatment
09:34

Microfluidic Pneumatic Cages: A Novel Approach for In-chip Crystal Trapping, Manipulation and Controlled Chemical Treatment

Published on: July 12, 2016

9.5K

Area of Science:

  • Control Systems Engineering
  • Nonlinear Process Control

Background:

  • Sliding Mode Control (SMC) is a robust control strategy.
  • Traditional SMC can exhibit chattering and may not be optimal for all operating regions.
  • Dual-mode control concepts offer potential for improved performance by adapting control gain.

Purpose of the Study:

  • To develop and evaluate a new dual-mode based Sliding Mode Controller (SMC).
  • To enhance tracking and regulation performance in nonlinear systems.
  • To compare the proposed controller against existing SMC alternatives.

Main Methods:

  • A novel dual-mode control law combining higher gain for large errors and lower gain for small errors was synthesized.
  • The controller design utilizes a dual-mode (PD-PID) sliding surface.
  • Simulations were performed on a continuous stirred-tank reactor (CSTR) and a mixing tank with variable dead time.

Main Results:

  • The dual-mode SMC demonstrated effective control for both setpoint changes and disturbance rejection.
  • Performance was evaluated using Integral of Time multiplied by Squared Error (ITSE), Total Variation of Control effort (TVu), Maximum Overshoot (Mp), and Settling Time (ts).
  • Radial graphs were used to compare the proposed controller's merits and drawbacks against alternative SMC methods.

Conclusions:

  • The proposed dual-mode based SMC offers a promising approach for improving control performance in nonlinear systems.
  • The controller effectively balances transient response and steady-state accuracy.
  • The dual-mode strategy provides a more refined control action compared to conventional SMC.