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

Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

381
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...
381
PI Controller: Design01:24

PI Controller: Design

1.1K
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...
1.1K
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

345
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...
345
PID Controller01:19

PID Controller

623
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...
623
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.5K
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...
1.5K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Perceptron-Based Adaptive Model Predictive Control for Stochastic Sampled-Data Unknown Nonlinear Systems.

IEEE transactions on cybernetics·2026
Same author

Dynamic Event-Triggered Control for Flexible Joint Robot Based on Fully Actuated System Approach.

IEEE transactions on cybernetics·2025
Same author

Adaptive Event-Triggered Control Combined With High-Order Backstepping for Pure Feedback Nonlinear Systems.

IEEE transactions on cybernetics·2025
Same author

A New Event-Triggered Adaptive Fixed-Time Control Design for Uncertain Nonlinear Systems.

IEEE transactions on cybernetics·2024
Same author

Event-Based Remote State Estimation for Nonlinear Systems: A Box Particle Filtering Method.

IEEE transactions on cybernetics·2022
Same author

Event-Triggered Adaptive Output Feedback Control for a Class of Uncertain Nonlinear Systems With Actuator Failures.

IEEE transactions on cybernetics·2018

Related Experiment Video

Updated: Jan 7, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

372

A New Neural Network PI-Funnel Distributed Control for Cooperative Manipulator With Global Prescribed Performance.

Cui-Hua Zhang, Ze-Yun Hu, Yu-Jia Li

    IEEE Transactions on Neural Networks and Learning Systems
    |January 1, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new control method for uncertain robotic systems, significantly reducing steady-state error oscillations. The approach uses neural networks and a dynamic funnel function for precise control without needing an exact system model.

    More Related Videos

    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
    10:51

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

    Published on: March 10, 2011

    14.2K
    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    5.0K

    Related Experiment Videos

    Last Updated: Jan 7, 2026

    Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
    05:30

    Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

    Published on: October 10, 2025

    372
    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
    10:51

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

    Published on: March 10, 2011

    14.2K
    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    5.0K

    Area of Science:

    • Robotics
    • Control Theory
    • Artificial Intelligence

    Background:

    • Distributed control systems face challenges with uncertain dynamics and steady-state errors.
    • Prescribed performance control aims to meet strict performance criteria but can be complex to implement.
    • Neural networks offer powerful approximation capabilities for handling system uncertainties.

    Purpose of the Study:

    • To develop a global distributed prescribed-performance control framework for uncertain Lagrangian dynamics.
    • To minimize steady-state error oscillations in robotic manipulator consensus control.
    • To integrate neural network design with dynamic funnel functions for enhanced control.

    Main Methods:

    • A novel control framework combining dynamic funnel functions and neural network design.
    • Development of a new neural network learning law incorporating funnel barrier properties and derivative information.
    • Inclusion of a projection operator in the learning law to ensure boundedness of weight estimates.

    Main Results:

    • The proposed framework ensures global arbitrary convergence rates and steady-state error bounds for trajectory consensus error.
    • Neural network approximation effectively mitigates uncertainties in controllers lacking precise models.
    • Significant reduction in steady-state error oscillations was achieved, surpassing existing methods.

    Conclusions:

    • The novel distributed control framework successfully achieves global prescribed performance for uncertain systems.
    • The integration of neural networks into distributed funnel control is a pioneering advancement.
    • Simulation results confirm the method's effectiveness in suppressing steady-state error oscillations.