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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

234
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...
234
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

323
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...
323
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

674
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
674
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

641
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
641
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

352
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
352
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.7K

You might also read

Related Articles

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

Sort by
Same author

High-Density Co-Ir-Co Triple-Atom Sites in Salphen-Fused Nanoribbons Break the Activity-Stability Dilemma in Alkaline Oxygen Evolution.

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

Computational Screening-Assisted Design of Bioinspired Artificial Metalloenzymes with Efficient Cascade Biocatalysis To Alleviate Cerebral Ischemia-Reperfusion Injury.

Journal of the American Chemical Society·2026
Same author

Homologous heteropolyaromatic covalent organic frameworks for enhancing photocatalytic hydrogen peroxide production and aerobic oxidation.

Nature communications·2025
Same author

Tailoring Multiple Coordination Environments of Cobalt-Only Ladder Organic Framework for Bifunctional Oxygen Electrocatalysis.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Densely populated macrocyclic dicobalt sites in ladder polymers for low-overpotential oxygen reduction catalysis.

Nature communications·2025
Same author

Finite-Time <i>H</i><sub>∞</sub> Controllers Design for Stochastic Time-Delay Markovian Jump Systems with Partly Unknown Transition Probabilities.

Entropy (Basel, Switzerland)·2024

Related Experiment Video

Updated: Dec 27, 2025

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

2.0K

Pareto optimal control of the mean-field stochastic systems by adaptive dynamic programming algorithm.

Yingying Ge1, Xikui Liu2, Yan Li3

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

ISA Transactions
|March 2, 2020
PubMed
Summary

This study introduces a model-free adaptive dynamic programming (ADP) algorithm for Pareto games in stochastic systems. The ADP algorithm effectively approximates Pareto optimal solutions without needing full system parameters, ensuring convergence and unique strategy determination.

Keywords:
Adaptive dynamic programmingMean-field systemOptimal controlPareto game

More Related Videos

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

5.0K
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

5.4K

Related Experiment Videos

Last Updated: Dec 27, 2025

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

2.0K
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

5.0K
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

5.4K

Area of Science:

  • Control Systems
  • Optimization Theory
  • Stochastic Processes

Background:

  • The Pareto game in continuous-time stochastic systems is complex to solve, especially without complete system models.
  • Existing model-based methods require full system parameter knowledge, limiting their practical application.
  • Approximate/adaptive dynamic programming (ADP) offers a potential solution for model-free optimization.

Purpose of the Study:

  • To develop and analyze a model-free adaptive dynamic programming (ADP) algorithm for solving the Pareto game in continuous-time stochastic systems.
  • To demonstrate that the ADP algorithm can approximate Pareto optimal solutions without requiring complete system parameters.
  • To confirm the convergence and uniqueness of the Pareto optimal strategy found by the ADP algorithm.

Main Methods:

  • Proposed a model-based online iterative algorithm and proved its convergence to Pareto efficient solutions.
  • Derived a model-free iterative equation and developed an ADP algorithm to solve it using online data.
  • Utilized convergence analysis to establish the unique determination of the Pareto optimal strategy by the ADP algorithm.

Main Results:

  • The model-based algorithm converges to Pareto efficient solutions but requires complete system parameters.
  • The developed model-free ADP algorithm yields the same solutions as the model-based approach, approximating the Pareto optimal solution.
  • Convergence analysis confirmed that the Pareto optimal strategy is uniquely determined by the ADP algorithm.

Conclusions:

  • The proposed adaptive dynamic programming (ADP) algorithm effectively solves the Pareto game for model-free continuous-time stochastic systems.
  • The ADP algorithm provides a feasible and robust method for approximating Pareto optimal solutions, overcoming the limitations of model-based approaches.
  • Simulation examples validated the practical feasibility and performance of the developed ADP algorithm.