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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

397
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,...
397
Observational Learning01:12

Observational Learning

1.2K
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...
1.2K
Reinforcement Schedules01:24

Reinforcement Schedules

685
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
685
Linearization and Approximation01:26

Linearization and Approximation

155
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
155
PI Controller: Design01:24

PI Controller: Design

1.5K
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...
1.5K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

832
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
832

You might also read

Related Articles

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

Sort by
Same author

CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2.

Journal of chemical information and modeling·2026
Same author

Development and validation of a kinematic hindlimb cycling model for rats.

Scientific reports·2025
Same author

Accelerated Gradient Approach For Deep Neural Network-Based Adaptive Control of Unknown Nonlinear Systems.

IEEE transactions on neural networks and learning systems·2024
Same author

Data-Based and Opportunistic Integral Concurrent Learning for Adaptive Trajectory Tracking During Switched FES-Induced Biceps Curls.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2022
Same author

Editorial: Safety in Collaborative Robotics and Autonomous Systems.

Frontiers in robotics and AI·2022
Same author

Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties.

Frontiers in robotics and AI·2022
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

Related Experiment Video

Updated: Mar 26, 2026

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

Model-Based Reinforcement Learning for Infinite-Horizon Approximate Optimal Tracking.

Rushikesh Kamalapurkar, Lindsey Andrews, Patrick Walters

    IEEE Transactions on Neural Networks and Learning Systems
    |February 11, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study presents an adaptive control method for nonlinear systems with unknown dynamics, using reinforcement learning to improve trajectory tracking. The approach ensures stability and policy convergence for optimal control performance.

    More Related Videos

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
    08:32

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

    Published on: June 15, 2020

    13.6K
    Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
    07:34

    Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

    Published on: November 7, 2025

    459

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    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
    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
    08:32

    Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

    Published on: June 15, 2020

    13.6K
    Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
    07:34

    Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

    Published on: November 7, 2025

    459

    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Machine Learning

    Background:

    • Optimal tracking is crucial for nonlinear control systems.
    • Unknown system dynamics pose significant challenges.
    • Traditional methods often require persistent excitation, limiting applicability.

    Purpose of the Study:

    • To develop an online adaptive solution for the infinite-horizon optimal tracking problem.
    • To address control-affine continuous-time nonlinear systems with unknown drift dynamics.
    • To relax the strict persistence of excitation condition.

    Main Methods:

    • Model-based reinforcement learning is employed.
    • A concurrent-learning-based system identifier simulates experience.
    • Bellman error evaluation over unexplored state-space areas is used.
    • Lyapunov-based stability analysis ensures theoretical guarantees.

    Main Results:

    • The proposed method achieves approximate online adaptive control.
    • Successful tracking of desired trajectories is demonstrated.
    • Convergence of the learned policy to a neighborhood of the optimal policy is established.
    • Simulation results validate the technique's effectiveness.

    Conclusions:

    • The developed technique offers a robust solution for optimal tracking in complex systems.
    • Relaxing persistence of excitation enhances practical applicability.
    • The integration of reinforcement learning and system identification is effective.