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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...
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...
Control Systems01:10

Control Systems

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...
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

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Related Experiment Video

Updated: Jun 19, 2026

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

Inverse Reinforcement Learning H ∞ Optimal Control for Takagi-Sugeno Fuzzy Systems.

Wenting Song, Yongming Li, Shaocheng Tong

    IEEE Transactions on Cybernetics
    |June 17, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces inverse reinforcement learning (RL) for fuzzy systems, enabling controllers to learn expert behavior and ensure stability. The method effectively controls autonomous surface vehicles (ASVs) by reconstructing cost functions and imitating expert actions.

    Related Experiment Videos

    Last Updated: Jun 19, 2026

    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

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Fuzzy Logic Systems

    Background:

    • Disturbances in control systems pose challenges for achieving optimal performance.
    • Imitating expert behavior is crucial for developing effective control strategies.
    • Takagi-Sugeno (T-S) fuzzy systems offer a framework for modeling complex nonlinear systems.

    Purpose of the Study:

    • To develop an inverse reinforcement learning (RL) H-infinity optimal control approach for T-S fuzzy systems.
    • To reconstruct the expert system's cost function and imitate its behavior.
    • To address scenarios where the learner system's dynamics are known or unknown.

    Main Methods:

    • A learner-expert framework with two inverse RL algorithms was proposed.
    • Algorithms involve optimal policy updates using game algebraic Riccati equations (GAREs).
    • Gradient descent correction and inverse optimal control iteration refine the control strategy.

    Main Results:

    • The developed algorithms were proven to be convergent.
    • The fuzzy inverse RL optimal control methodology ensures asymptotic stability.
    • A Nash equilibrium solution was achieved, demonstrating effective control.

    Conclusions:

    • The proposed fuzzy H-infinity optimal control method is effective for systems with disturbances.
    • Application to an autonomous surface vehicle (ASV) system validated the methodology.
    • The approach successfully reconstructs expert cost functions and imitates expert behavior.