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

123
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...
123
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

85
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,...
85
PD Controller: Design01:26

PD Controller: Design

250
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,...
250
Feedback control systems01:26

Feedback control systems

319
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...
319
PI Controller: Design01:24

PI Controller: Design

300
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...
300
Control Systems01:10

Control Systems

1.2K
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...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Effectiveness of Fitbit-Based Interventions in Improving 24-hour Movement Behaviors: A Systematic Review and Meta-Analysis.

Inquiry : a journal of medical care organization, provision and financing·2026
Same author

Photoinduced, Energy-Transfer-Mediated Synergistic N,S-Difunctionalization of Alkenes with CN Migration.

Organic letters·2026
Same author

Expression and ligand-binding characterization of two chemosensory proteins (TabsCSP8 and TabsCSP19) in Tuta absoluta.

Pesticide biochemistry and physiology·2026
Same author

Histone chaperone HIRA restrains ferroptosis susceptibility through epigenetic control of iron metabolism in prostate cancer.

Science China. Life sciences·2026
Same author

The legs of the young heart stool: how motor competence, muscle strength, and physical activity support cardiorespiratory fitness in preschoolers?

Frontiers in sports and active living·2026
Same author

Cytoreductive radical prostatectomy in patients with high-volume metastatic prostate cancer achieving deep biochemical response to contemporary systemic therapy: a multicentre, prospective cohort study.

BMC medicine·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

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

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

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

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

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

A Survey on Human-Centric Voice-Face Multimodal Learning.

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

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

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

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jul 13, 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

5.0K

Data-Driven Optimal Tracking Control for Discrete-Time Nonlinear Systems With Unknown Dynamics Using Deterministic

Shijie Song, Dawei Gong, Minglei Zhu

    IEEE Transactions on Neural Networks and Learning Systems
    |October 17, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new data-driven algorithm for optimal tracking in nonlinear systems. It uses input-output data to achieve guaranteed performance, saving time and improving robustness.

    More Related Videos

    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

    1.7K
    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

    13.8K

    Related Experiment Videos

    Last Updated: Jul 13, 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

    5.0K
    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

    1.7K
    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

    13.8K

    Area of Science:

    • Control Systems Engineering
    • Machine Learning
    • Robotics

    Background:

    • Optimal tracking problem (OTP) for discrete-time (DT) nonlinear systems with unknown dynamics is challenging.
    • Existing data-driven deterministic approximate dynamic programming (ADP) methods for OTP have limitations.

    Purpose of the Study:

    • To propose a novel data-driven deterministic ADP algorithm for solving the OTP in DT nonlinear systems using only input-output (I/O) data.
    • To address limitations of existing algorithms by enhancing performance, robustness, and implementation simplicity.

    Main Methods:

    • Development of a novel data-driven deterministic approximate dynamic programming (ADP) algorithm.
    • Utilizing only input-output (I/O) data for learning control policies.
    • Theoretical analysis to prove convergence and stability, considering neural network (NN) errors.

    Main Results:

    • The proposed algorithm guarantees optimality and offers improved time-saving and data robustness.
    • Learned control policies simplify implementation by not requiring expected control.
    • Demonstrated effectiveness and advantages through numerical examples and application to a two-link robotic manipulator.

    Conclusions:

    • The developed data-driven deterministic ADP algorithm effectively solves the optimal tracking problem for discrete-time nonlinear systems.
    • The algorithm offers significant advantages in performance, robustness, and implementation simplicity compared to existing methods.
    • Theoretical guarantees of convergence and stability are established, validating the approach.