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

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...
Controller Configurations01:22

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

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 and...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
PD Controller: Design01:26

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: Design01:24

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

Effectiveness of aroma massage on advanced cancer patients with constipation: a pilot study.

Complementary therapies in clinical practice·2010
Same author

Synthesizing two-fingered grippers for positioning and identifying objects.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008
See all related articles

Related Experiment Video

Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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

Published on: October 14, 2017

Methodological development of fuzzy-logic controllers from multivariable linear control.

S K Tso1, Y H Fung

  • 1Center for Intelligent Design, Autom. & Manuf., City Univ. of Hong Kong, Kowloon.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

A new seven-step linear-to-fuzzy (LIN2FUZ) algorithm mechanizes fuzzy-logic controller design. This method generates fuzzy labels and rules from linear controllers, enabling enhanced performance beyond conventional systems.

Related Experiment Videos

Last Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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 Systems Engineering
  • Computational Intelligence
  • Automation

Background:

  • Designing fuzzy-logic controllers (FLCs) traditionally relies on expert knowledge and heuristic methods.
  • Mechanizing the initial FLC design, especially for performance comparable to linear controllers, remains challenging.
  • Conventional controllers like state-feedback and PID controllers are widely used but have limitations.

Purpose of the Study:

  • To introduce a systematic algorithm for the automated design of fuzzy-logic controllers.
  • To enable the generation of functionally equivalent FLCs from existing linear controller designs.
  • To provide a foundation for developing FLCs with performance exceeding conventional controllers.

Main Methods:

  • A novel seven-step linear-to-fuzzy (LIN2FUZ) algorithm is proposed.
  • The algorithm systematically generates linguistic labels, universes of discourse, and fuzzy rules.
  • It utilizes the design of available conventional multivariable linear controllers as a reference.

Main Results:

  • The LIN2FUZ algorithm successfully generates fuzzy-logic controllers from linear controller designs.
  • The developed FLCs are functionally equivalent to their linear counterparts.
  • Demonstrated effectiveness on a four-input, one-output inverted pendulum system.

Conclusions:

  • The LIN2FUZ algorithm offers a systematic and mechanized approach to FLC design.
  • This method facilitates the creation of advanced FLCs with potential for superior performance.
  • The approach bridges the gap between traditional linear control and modern fuzzy logic control.