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-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

474
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...
474
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
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...
1.2K
PD Controller: Design01:26

PD Controller: Design

744
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,...
744
Feedback control systems01:26

Feedback control systems

811
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...
811
Open and closed-loop control systems01:17

Open and closed-loop control systems

2.1K
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...
2.1K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

888
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
888

You might also read

Related Articles

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

Sort by
Same author

Cross-sector deep learning scales life cycle assessment using unified textual descriptions.

Environmental science and ecotechnology·2026
Same author

Development of APH003─a Highly Potent, Selective, and Orally Bioavailable IRAK4 PROTAC Degrader for the Treatment of Inflammatory Diseases.

Journal of medicinal chemistry·2026
Same author

[Retracted] Acaricidal activity of extracts from <i>Ligularia virgaurea</i> against the <i>Sarcoptes scabiei</i> mite <i>in vitro</i>.

Experimental and therapeutic medicine·2026
Same author

Approximate Optimal Control for Morphing Aircraft via Attention Meta-Learning and Continual Learning.

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

Recent Advances on Off-Policy Reinforcement Learning for Optimization Control.

IEEE transactions on cybernetics·2026
Same author

Optimal cooperative output regulation with norm-based performance specifications.

ISA transactions·2026

Related Experiment Video

Updated: Apr 16, 2026

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

5.3K

Adaptive optimal control of highly dissipative nonlinear spatially distributed processes with neuro-dynamic

Biao Luo, Huai-Ning Wu, Han-Xiong Li

    IEEE Transactions on Neural Networks and Learning Systems
    |March 21, 2015
    PubMed
    Summary

    This study introduces an adaptive optimal control approach using neuro-dynamic programming (NDP) for complex industrial systems described by partial differential equations (PDEs). The method effectively solves the Hamilton-Jacobi-Bellman (HJB) equation, ensuring system stability and demonstrating practical applications.

    More Related Videos

    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

    5.5K

    Related Experiment Videos

    Last Updated: Apr 16, 2026

    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

    5.3K
    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

    5.5K

    Area of Science:

    • Control Engineering
    • Applied Mathematics
    • Computational Science

    Background:

    • Industrial spatially distributed processes (SDPs) often involve highly dissipative nonlinear partial differential equations (PDEs).
    • Optimal control of these complex systems presents significant analytical and computational challenges.
    • Existing methods struggle with the inherent nonlinearity and infinite dimensionality of PDEs.

    Purpose of the Study:

    • To develop an adaptive optimal control strategy for general highly dissipative nonlinear PDEs governing industrial SDPs.
    • To address the challenge of solving the analytically intractable Hamilton-Jacobi-Bellman (HJB) equation for optimal control.
    • To ensure the stability and boundedness of the closed-loop system under the proposed adaptive control policy.

    Main Methods:

    • Karhunen-Loève decomposition (method of snapshots) to derive empirical eigenfunctions (EEFs).
    • Singular perturbation technique to obtain a finite-dimensional slow subsystem from the PDE.
    • Neuro-dynamic programming (NDP) with neural networks (NNs) for online HJB equation solution and adaptive control policy generation.

    Main Results:

    • A novel adaptive optimal control approach based on NDP is proposed for highly dissipative nonlinear PDEs.
    • The method successfully reformulates the optimal control problem via a slow subsystem and solves the HJB equation online using NNs.
    • Stability analysis proves semiglobal uniform ultimate boundedness of the closed-loop PDE system, incorporating NN estimation error.

    Conclusions:

    • The developed adaptive optimal control method effectively manages complex industrial spatially distributed processes governed by nonlinear PDEs.
    • The neuro-dynamic programming approach provides a robust solution for the Hamilton-Jacobi-Bellman equation without requiring an initial stabilizing policy.
    • The method's efficacy is validated through simulations on a nonlinear diffusion-convection-reaction process and a practical aerospace application.