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

PD Controller: Design01:26

PD Controller: Design

341
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,...
341
PI Controller: Design01:24

PI Controller: Design

470
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...
470
Control Systems01:10

Control Systems

1.4K
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...
1.4K
Second Order systems I01:20

Second Order systems I

228
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
228
Open and closed-loop control systems01:17

Open and closed-loop control systems

972
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...
972
Feedback control systems01:26

Feedback control systems

413
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...
413

You might also read

Related Articles

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

Sort by
Same author

Sensory-Cognitive Profiles in Children with ADHD: Exploring Perceptual-Motor, Auditory, and Oculomotor Function.

Bioengineering (Basel, Switzerland)·2025
Same author

The Integration of Artificial Intelligence with Micro-Nano-Systems: Perspectives, Challenges and Future Prospects.

Micromachines·2025
Same author

Model Parametrization-Based Genetic Algorithms Using Velocity Signal and Steady State of the Dynamic Response of a Motor.

Biomimetics (Basel, Switzerland)·2025
Same author

Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques.

Biomimetics (Basel, Switzerland)·2025
Same author

Perceptual-Motor Abilities and Reversal Frequency of Letters and Numbers in Children Diagnosed with Poor Reading Skills.

Bioengineering (Basel, Switzerland)·2025
Same author

Deciphering the Physical Characteristics of Ophthalmic Filters Used in Optometric Vision Therapy.

Healthcare (Basel, Switzerland)·2024

Related Experiment Video

Updated: Sep 6, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.8K

Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA.

Marco-Antonio Cabrera-Rufino1, Juan-Manuel Ramos-Arreguín1, Juvenal Rodríguez-Reséndiz1

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas, Las Campanas, Queretaro 76010, Mexico.

Micromachines
|June 24, 2022
PubMed
Summary

This study introduces an intelligent controller for pneumatic robot manipulators, addressing non-linearities for improved industrial applications. The AI controller enhances performance with a minimum mean error of ±1.2 mm.

Keywords:
FPGAcontrolembeddedneural networkneuro-PIDpneumatic actuatorsrobot arm

More Related Videos

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K
Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers
07:09

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Published on: August 17, 2018

9.2K

Related Experiment Videos

Last Updated: Sep 6, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.8K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K
Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers
07:09

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Published on: August 17, 2018

9.2K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Pneumatic robot manipulators offer advantages like clean operation and high power-to-weight ratios for industrial use.
  • However, pneumatic actuators exhibit non-linear characteristics, posing control challenges.
  • Existing methods struggle to effectively manage these non-linearities in complex robotic systems.

Purpose of the Study:

  • To propose and evaluate an intelligent controller for a 3-degrees-of-freedom pneumatic robot.
  • To minimize the non-linear behavior inherent in pneumatic actuators.
  • To enhance the precision and responsiveness of pneumatic robotic systems in industrial settings.

Main Methods:

  • An intelligent controller was embedded within a programmable logic device.
  • The controller utilized neural networks with three neurons per degree of freedom to adjust controller gains.
  • A continuous learning process tuned gain values to minimize mean square error.

Main Results:

  • The proposed intelligent controller demonstrated a more appropriate system behavior during transitive time.
  • The controller effectively minimized non-linearities in the pneumatic robot's air behavior.
  • A minimum mean error of ±1.2 mm was achieved, indicating high precision.

Conclusions:

  • The intelligent controller successfully addresses the non-linearities of pneumatic actuators.
  • This AI-driven approach enhances the performance and accuracy of pneumatic robot manipulators.
  • The findings support the use of intelligent control in industrial applications requiring precise pneumatic actuation.