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

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...

Controller Configurations

Controller Configurations

Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...

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,...

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...

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...

Time and frequency -Domain Interpretation of PI Control

Time and frequency -Domain Interpretation of PI Control

Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires careful...

You might also read

Related Articles

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

Sort by

Same author

In situ scattering of single gold nanorod coupling with monolayer transition metal dichalcogenides.

Nanoscale·2019

Same author

Facile Synthesis of GNPs@Ni<sub>x</sub>S<sub>y</sub>@MoS<sub>2</sub> Composites with Hierarchical Structures for Microwave Absorption.

Nanomaterials (Basel, Switzerland)·2019

Same author

Efficacy and Safety of Oral Janus Kinase 1 Inhibitor Abrocitinib for Patients With Atopic Dermatitis: A Phase 2 Randomized Clinical Trial.

JAMA dermatology·2019

Same author

Association between BHMT and CBS gene promoter methylation with the efficacy of folic acid therapy in patients with hyperhomocysteinemia.

Journal of human genetics·2019

Same author

Effects of inorganic nitrogen and litters of Masson Pine on soil organic carbon decomposition.

PloS one·2019

Same author

Liver-expressed antimicrobial peptide 2 antagonizes the effect of ghrelin in rodents.

The Journal of endocrinology·2019

Same journal

A robust ATUB-Net for bearing fault diagnosis under unbalanced sample scenarios.

ISA transactions·2026

Same journal

Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism.

ISA transactions·2026

Same journal

Predefined-time affine formation tracking control of unmanned surface vehicles with input saturation via adaptive fuzzy observers.

ISA transactions·2026

Same journal

Adaptive fault-tolerant safety-guaranteed fuzzy event-triggered rendezvous control for heterogeneous USV-UUV systems.

ISA transactions·2026

Same journal

Two-stage maximum likelihood weighted recursive least squares algorithm for nonlinear systems and an application in wind tunnel systems.

ISA transactions·2026

Same journal

Enhancing interpretable soft sensing with embedded hybrid modeling: the GraphTrans approach for industrial processes.

ISA transactions·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Optimal solution, quantitative performance estimation, and robust tuning of the simplifying controller.

Weidong Zhang¹, Xiaoming Xu

¹Department of Automation, Shanghai Jiaotong University, People's Republic of China. wdzhang@mail.sjtu.edu.cn

ISA Transactions

|May 17, 2002

Summary

This summary is machine-generated.

A new simplifying controller improves control for processes with large time delays. This study provides a comprehensive analysis, design procedure, and tuning method for this advanced control system.

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jun 14, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

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

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

Area of Science:

Control Engineering
Process Control

Background:

Processes with significant time delays pose challenges for traditional control systems.
Existing methods like the Smith predictor and internal model control (IMC) have limitations in simplifying complex control structures.

Purpose of the Study:

To provide a comprehensive analysis of a recently proposed simplifying controller.
To establish the relationship between the simplifying controller, Smith predictor, and IMC.
To develop an analytical design procedure and robust tuning method for the simplifying controller.

Main Methods:

Comparative analysis of control schemes: simplifying controller, Smith predictor, and IMC.
Development of an analytical design procedure based on IMC principles.
Derivation of an optimal control solution.
Quantitative estimation of closed-loop system time-domain performance.
Presentation of a robust tuning procedure.

Main Results:

The relationship between the simplifying controller, Smith predictor, and IMC is elucidated.
An optimal analytical design procedure for the simplifying controller is derived using IMC.
Methods for quantitatively assessing time-domain performance are presented.
A straightforward and robust tuning procedure is introduced.

Conclusions:

The simplifying controller offers a viable and effective approach for processes with large time delays.
The developed design and tuning procedures enhance the practical application of the simplifying controller.
This work provides a thorough understanding and practical tools for implementing advanced process control.