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

416
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...
416
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
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,...
124
Second Order systems II01:18

Second Order systems II

171
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
171
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

178
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...
178
Control System Problem01:21

Control System Problem

175
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
175
PD Controller: Design01:26

PD Controller: Design

349
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,...
349

You might also read

Related Articles

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

Sort by
Same author

Thermal Conductivity and Dielectric Properties of EP Composites Enhanced by BNNS-AgNP Synergistic Doping.

Nanomaterials (Basel, Switzerland)·2026
Same author

Effect of Micro and Nano Boron Nitride on Thermal Conductivity and Electrical Properties of Mica Tape.

Materials (Basel, Switzerland)·2026
Same author

Domain knowledge embedded anti-disturbance autonomous navigation for marine vehicles.

Communications engineering·2026
Same author

Amphiphilic xanthotoxin derivatives with phosphatidylglycerol-targeting membrane disruption for potent anti-methicillin-resistant Staphylococcus aureus (MRSA) activity.

European journal of medicinal chemistry·2026
Same author

The mechanism of enhancing material insulation performance through homogenizing free volume distribution mediated by a micro-branched structure.

Journal of colloid and interface science·2026
Same author

Multiple Distributed PVs Participating in Active Power Support Under Resource Aggregation and Data Communication Congestion.

IEEE transactions on cybernetics·2025
Same journal

A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths.

IEEE transactions on cybernetics·2026
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 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 Backstepping Control for a Class of Unknown Nonlinear Strict-Feedback Systems.

Wei Wang, Songlin Hu, Dong Yue

    IEEE Transactions on Cybernetics
    |September 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a data-driven backstepping control (DBC) method for strict-feedback systems. It avoids online models, ensuring system stability using off-line data and novel controllers.

    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

    1.8K
    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: Sep 9, 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

    1.8K
    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
    • Robotics
    • Machine Learning

    Background:

    • Tracking control for strict-feedback systems with unknown dynamics is challenging.
    • Existing methods often rely on online models and assumptions, limiting applicability.
    • A need exists for data-driven control strategies that bypass online model identification.

    Purpose of the Study:

    • To propose a data-driven backstepping control (DBC) approach for strict-feedback systems with unknown dynamics.
    • To address the complexity explosion issue in DBC through a data-driven dynamic surface control (DDSC) method.
    • To validate the effectiveness of the proposed data-driven control strategies.

    Main Methods:

    • Developed a data-driven continuous-time Lyapunov equation return controller using off-line data.
    • Proposed a data-driven dynamic surface control (DDSC) approach utilizing a data-driven LMI.
    • Ensured semi-global exponential stability and semi-globally uniformly ultimately bounded (UUB) error systems.

    Main Results:

    • The data-driven backstepping control (DBC) approach successfully identified unknown dynamics from off-line data.
    • The data-driven dynamic surface control (DDSC) mitigated the complexity explosion problem.
    • Both DBC and DDSC demonstrated semi-global exponential stability and semi-global UUB error systems.

    Conclusions:

    • The proposed data-driven control methods offer effective solutions for strict-feedback systems with unknown dynamics.
    • These approaches eliminate the need for online approximation models, simplifying control design.
    • Simulation examples confirm the superiority and effectiveness of the data-driven control strategies.