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

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,...

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...

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-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...

Phase-lead and Phase-lag Controllers

Phase-lead and Phase-lag Controllers

Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass filters, manage...

PID Controller

PID Controller

Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...

You might also read

Related Articles

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

Sort by

Same author

Radiographic Evaluation of Periapical Healing Following Single vs Multiple Visit Root Canal Treatment: A PAI Based Study.

Journal of pharmacy & bioallied sciences·2026

Same author

Reversal of optic disc cupping following trabeculectomy.

Journal francais d'ophtalmologie·2025

Same author

Cost-effective synthesis of zinc oxide/crab shell-derived chitosan nanocomposite: Insights into its biomedical applications.

International journal of biological macromolecules·2024

Same author

Fluorophore-quencher complexes (Cu<sup>2+</sup>/Al<sup>3+</sup>) of coumarin Schiff bases as chemosensors for the detection of L-glutamic acid and L-arginine: <i>in vitro</i> and <i>in vivo</i> studies.

Dalton transactions (Cambridge, England : 2003)·2024

Same author

Classic anterior lenticonus in Alport syndrome.

Journal francais d'ophtalmologie·2024

Same author

Superconductivity coupling of harmonic resonant oscillators: Homogeneous and heterogeneous extreme multistability with multi-scrolls.

Chaos (Woodbury, N.Y.)·2024

Search research articles

Related Experiment Video

Updated: May 15, 2026

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

Fuzzy scheduled RTDA controller design.

K Srinivasan¹, K Anbarasan

¹National Institute of Technology, Department of Instrumentation and Control Engineering, Tiruchirappalli, Tamil Nadu, India. srinikkn@nitt.edu

ISA Transactions

|January 16, 2013

Summary

This summary is machine-generated.

A novel fuzzy scheduled controller enhances robustness, tracking, and disturbance rejection for non-linear processes like pH neutralization. This advanced control strategy outperforms traditional methods in challenging industrial applications.

Related Experiment Videos

Last Updated: May 15, 2026

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:

Chemical Engineering
Control Systems Engineering
Process Control

Background:

Non-linear processes, such as pH neutralization, present significant control challenges due to inherent gain variations.
Designing effective controllers for these systems requires advanced strategies to maintain stability and performance.

Purpose of the Study:

To design and develop a fuzzy scheduled robustness, tracking, disturbance rejection, and overall aggressiveness (RTDA) controller.
To evaluate the controller's performance on non-linear processes, specifically pH neutralization, type I diabetic, and conical tank systems.

Main Methods:

Implementation of a fuzzy scheduled RTDA controller utilizing the normalized integral square error (N_ISE) performance criteria.
Comparative analysis of the proposed controller against established Internal Model Control (IMC) and Dynamic Matrix Control (DMC) schemes.

Main Results:

The fuzzy scheduled RTDA controller demonstrated effective control for non-linear pH neutralization processes.
The controller's applicability was validated across diverse non-linear systems, including type I diabetic and conical tank processes.
Performance evaluation showed competitive or superior servo and regulatory control compared to IMC and DMC.

Conclusions:

The fuzzy scheduled RTDA controller offers a robust and effective solution for managing non-linear processes with significant gain variations.
This control strategy provides a viable alternative to existing advanced control methods, particularly in demanding industrial environments.