Jove
Visualize
Contact Us

Related Concept Videos

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

984
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
984
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

194
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...
194
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

116
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
116
Feedback control systems01:26

Feedback control systems

474
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...
474
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

191
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
191

You might also read

Related Articles

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

Sort by
Same author

Editorial: Safety in Collaborative Robotics and Autonomous Systems.

Frontiers in robotics and AI·2022
Same author

The State Following Approximation Method.

IEEE transactions on neural networks and learning systems·2018
Same author

Approximate Dynamic Programming: Combining Regional and Local State Following Approximations.

IEEE transactions on neural networks and learning systems·2018
Same author

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

IEEE transactions on neural networks and learning systems·2016
Same author

Identification-Based Closed-Loop NMES Limb Tracking With Amplitude-Modulated Control Input.

IEEE transactions on cybernetics·2015
Same author

Approximate N-Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System.

IEEE transactions on neural networks and learning systems·2014
Same journal

Passive wheels on legged robots: a survey.

Frontiers in robotics and AI·2026
Same journal

Politeness cannot make up for robots' errors.

Frontiers in robotics and AI·2026
Same journal

Workers expect basic social skills but limited autonomy from future robots - a qualitative interview study and taxonomy for robot social skills.

Frontiers in robotics and AI·2026
Same journal

Human-robot interaction in sustainable hospitality: how robot type shapes customer emotions, green perceptions, and service loyalty.

Frontiers in robotics and AI·2026
Same journal

Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.

Frontiers in robotics and AI·2026
Same journal

Editorial: Synergizing large language models and computational intelligence for advanced robotic systems.

Frontiers in robotics and AI·2026
See all related articles
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 Experiment Video

Updated: Oct 8, 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

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

S M Nahid Mahmud1, Scott A Nivison2, Zachary I Bell2

  • 1School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, United States.

Frontiers in Robotics and AI
|January 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a safe reinforcement learning method for complex systems with unknown parameters. It enables learning optimal control policies while ensuring system safety and avoiding strict learning conditions.

Keywords:
barrier transformationcontrol theorymodel-based reinforcement learningnonlinear systemsparameter estimationsafe learning

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K
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: Oct 8, 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
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K
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
  • Machine Learning
  • Dynamical Systems

Background:

  • Reinforcement learning (RL) is effective for optimal control but often lacks safety guarantees.
  • Safety constraints are crucial for safety-critical systems where restarts are infeasible.
  • Existing RL methods for state constraints often require persistent excitation or exact model knowledge.

Purpose of the Study:

  • Develop a safe reinforcement learning method for deterministic nonlinear systems with parametric uncertainties.
  • Learn approximate constrained optimal control policies without stringent excitation conditions.
  • Address safety during learning and execution in safety-critical applications.

Main Methods:

  • Utilizes a model-based reinforcement learning technique.
  • Integrates a novel filtered concurrent learning method for parameter identification.
  • Employs a barrier transformation for enforcing state constraints.
  • Simultaneously learns unknown model parameters and control policies.

Main Results:

  • Successfully learns approximate optimal control policies under state constraints.
  • Accommodates parametric uncertainties in the system model.
  • Avoids reliance on persistent excitation conditions during learning.
  • Ensures safety throughout the learning and execution phases.

Conclusions:

  • The proposed method offers a robust approach to safe reinforcement learning for uncertain nonlinear systems.
  • It enhances the applicability of RL in safety-critical domains.
  • The technique effectively balances learning efficiency with safety guarantees.