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

462
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...
462
Controller Configurations01:22

Controller Configurations

171
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
171
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.1K
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...
1.1K
Observational Learning01:12

Observational Learning

348
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
348
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

178
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
178
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.9K

You might also read

Related Articles

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

Sort by
Same author

The chromosome-level genome assembly of Malus robusta (Rosales: Rosaceae).

Scientific data·2026
Same author

Sunitinib induces immunogenic cell death through eIF2α phosphorylation to potentiate immunotherapy in HCC.

iScience·2026
Same author

Bio-inspired fractal-structured gel-drugs enable enhancing deep tumor penetration for efficient chemotherapy of hepatocellular carcinoma.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Lemon-Derived Extracellular Vesicles Engineered Oral Capsules for Enhanced Colorectal Cancer Chemotherapy by Mechanical Stress-Induced Intestinal Epithelial Barrier Opening.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Structural basis for the FOXM1 DNA binding domain to specific dsDNA substrate.

Acta biochimica et biophysica Sinica·2026
Same author

Synergistic Reinforcement and Multimodal Self-Sensing Properties of Hybrid Fiber-Reinforced Glass Sand ECC at Elevated Temperatures.

Polymers·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

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

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

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

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Related Experiment Video

Updated: Sep 27, 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

5.1K

Adaptive Learning Control Algorithms for Infinite-Duration Tracking.

Mingxuan Sun, Shengxiang Zou

    IEEE Transactions on Neural Networks and Learning Systems
    |April 8, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces adaptive learning control (ALC) for infinite-duration tasks, avoiding periodicity and numerical integration. The new incremental approach offers a viable alternative for adaptive system designs, simplifying controller implementation.

    More Related Videos

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
    11:18

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

    Published on: June 1, 2015

    10.8K
    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

    4.7K

    Related Experiment Videos

    Last Updated: Sep 27, 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

    5.1K
    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
    11:18

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

    Published on: June 1, 2015

    10.8K
    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

    4.7K

    Area of Science:

    • Control Theory
    • Adaptive Systems
    • Machine Learning

    Background:

    • Learning control typically requires periodic or repeatable operations.
    • Existing methods lack approaches for infinite-duration tracking without these constraints.

    Purpose of the Study:

    • To develop an adaptive learning control (ALC) methodology for systems performing infinite-duration tasks.
    • To address the limitations of traditional learning control by removing periodicity and repeatability requirements.

    Main Methods:

    • Employed incremental adaptive mechanisms to circumvent numerical integration.
    • Utilized an error-tracking approach with approximation-based backstepping design for strict-feedback systems.
    • Introduced a novel integral Lyapunov function to handle state-dependent control gain.

    Main Results:

    • Demonstrated that incremental adaptation is an effective alternative to integral adaptation.
    • Established robust convergence of the tracking error.
    • Characterized the boundedness of closed-loop system variables.
    • Showcased satisfactory tracking performance and simplified controller design.

    Conclusions:

    • The proposed ALC method effectively handles infinite-duration tasks without periodicity.
    • The incremental adaptive mechanisms simplify implementation by avoiding numerical integration.
    • This approach offers a promising alternative for adaptive system design and control.