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

563
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...
563
Control Systems01:10

Control Systems

1.6K
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...
1.6K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.3K
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...
1.3K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

246
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
246
Linear time-invariant Systems01:23

Linear time-invariant Systems

691
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
691
Classification of Systems-II01:31

Classification of Systems-II

376
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
376

You might also read

Related Articles

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

Sort by
Same author

Corrections to "A Fully Data-Driven Value Iteration for Stochastic LQR: Convergence, Robustness, and Stability".

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

Clinical characteristics and treatment patterns in ankylosing spondylitis patients undergoing total hip arthroplasty: a single-center retrospective cohort study with a two-decade comparative analysis (2001-2023).

BMC musculoskeletal disorders·2026
Same author

Cuevaenes F-H, Including Two Rare Chlorinated Cuevaenes From the Marine-Derived Streptomyces malaysiensis HNM0561.

Chemistry & biodiversity·2026
Same author

Discrepancies in CPAK classification between CT and long-leg radiography: a systematic review and meta-analysis.

Skeletal radiology·2026
Same author

[Research progress on the role of macrophage efferocytosis in chronic obstructive pulmonary disease].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology·2026
Same author

A Fully Data-Driven Value Iteration for Stochastic LQR: Convergence, Robustness, and Stability.

IEEE transactions on neural networks and learning systems·2026

Related Experiment Video

Updated: Nov 22, 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

2.0K

Reinforcement Learning and Adaptive Optimal Control for Continuous-Time Nonlinear Systems: A Value Iteration

Tao Bian, Zhong-Ping Jiang

    IEEE Transactions on Neural Networks and Learning Systems
    |January 8, 2021
    PubMed
    Summary

    This study introduces a novel continuous-time value iteration (VI) method for adaptive optimal control in nonlinear systems. This approach enables learning robust controllers even with unknown system dynamics, advancing dynamic programming 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

    5.2K
    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

    2.8K

    Related Experiment Videos

    Last Updated: Nov 22, 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

    2.0K
    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

    5.2K
    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

    2.8K

    Area of Science:

    • Control Theory
    • Dynamic Systems
    • Machine Learning

    Background:

    • Traditional value iteration (VI) methods are limited to discrete-time systems and Markov decision processes.
    • Optimal control for continuous-time nonlinear systems with unknown dynamics remains a significant challenge.
    • Existing methods often require prior knowledge of system dynamics or an initial control policy.

    Purpose of the Study:

    • To develop a new continuous-time value iteration (VI) method for solving optimal control problems in continuous-time nonlinear systems.
    • To address the limitations of existing VI methods by extending their applicability to continuous-time dynamical systems.
    • To derive adaptive optimal controllers for systems with entirely unknown dynamics.

    Main Methods:

    • Development of a novel continuous-time value iteration (VI) algorithm based on Bellman's dynamic programming principles.
    • Application of the continuous-time VI method to adaptive and nonadaptive optimal control problems.
    • Formulation of a learning-based control algorithm for acquiring optimal controllers from real-time data.

    Main Results:

    • A new continuous-time VI method is successfully developed and applied to nonlinear systems.
    • Adaptive optimal controllers are derived for nonlinear systems with completely unknown dynamics.
    • A learning-based algorithm demonstrates the ability to learn robust optimal controllers directly from data.

    Conclusions:

    • The proposed continuous-time VI method effectively solves adaptive optimal control problems for continuous-time nonlinear systems.
    • The approach eliminates the need for an initial admissible control policy, simplifying controller design.
    • The methodology offers a powerful tool for developing robust and adaptive control strategies for complex systems.