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

496
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...
496
Effects of feedback01:24

Effects of feedback

755
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
755
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

203
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...
203
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.1K
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...
1.1K
PI Controller: Design01:24

PI Controller: Design

596
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...
596
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

145
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
145

You might also read

Related Articles

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

Sort by
Same author

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same author

Flavor enhancement of Yunnan Arabica coffee via Kombucha yeast consortium fermentation: microbial dynamics and physicochemical transformations.

Food science and biotechnology·2026
Same author

Prescribed-rate target tracking for time-delayed systems using output measurements.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Differential therapeutic implications of the OSM-OSMR signaling pathway in cancer and inflammatory diseases.

International journal of biological macromolecules·2025
Same author

Inverse Reinforcement Learning for Disturbed Networked Nonlinear Systems With Data Dropouts.

IEEE transactions on neural networks and learning systems·2025
Same author

Histone Lactylation-Driven Upregulation of VRK1 Expression Promotes Stemness and Proliferation of Glioma Stem Cells.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Related Experiment Video

Updated: Oct 16, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.1K

Data-Driven H∞ Optimal Output Feedback Control for Linear Discrete-Time Systems Based on Off-Policy Q-Learning.

Li Zhang, Jialu Fan, Wenqian Xue

    IEEE Transactions on Neural Networks and Learning Systems
    |October 18, 2021
    PubMed
    Summary

    This study introduces novel Q-learning algorithms for H-infinity static output feedback control in linear discrete-time systems. These methods effectively solve control problems for unknown systems and are robust to probing noise.

    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

    13.9K
    Interactive and Visualized Online Experimentation System for Engineering Education and Research
    08:35

    Interactive and Visualized Online Experimentation System for Engineering Education and Research

    Published on: November 24, 2021

    2.7K

    Related Experiment Videos

    Last Updated: Oct 16, 2025

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
    08:18

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

    Published on: August 15, 2020

    5.1K
    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

    13.9K
    Interactive and Visualized Online Experimentation System for Engineering Education and Research
    08:35

    Interactive and Visualized Online Experimentation System for Engineering Education and Research

    Published on: November 24, 2021

    2.7K

    Area of Science:

    • Control Systems Engineering
    • Machine Learning
    • System Identification

    Background:

    • H-infinity control is crucial for robust system performance.
    • Output feedback control is essential when not all states are measurable.
    • Q-learning offers a powerful framework for reinforcement learning-based control.

    Purpose of the Study:

    • To develop novel on-policy and off-policy Q-learning algorithms for H-infinity static output feedback control.
    • To address the challenge of controlling linear discrete-time systems with completely unknown dynamics.
    • To establish conditions for the existence of optimal solutions under disturbance attenuation.

    Main Methods:

    • Development of a new output feedback control algorithm form.
    • Application of on-policy and off-policy Q-learning.
    • Rigorous mathematical proofs for convergence, difference, and equivalence of algorithms.
    • Analysis of probing noise effects on persistence of excitation.

    Main Results:

    • Successful development of two novel Q-learning algorithms for H-infinity static OPFB control.
    • Conditions for optimal OPFB solution existence are derived.
    • Convergence of both algorithms is rigorously proven.
    • The off-policy algorithm demonstrates immunity to probing noise, avoiding solution bias.

    Conclusions:

    • The proposed Q-learning algorithms provide an effective solution for H-infinity static OPFB control of unknown linear discrete-time systems.
    • The off-policy approach offers enhanced robustness against probing noise.
    • Simulation results validate the effectiveness of the developed control strategies.