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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

249
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
249
Data: Types and Distribution01:19

Data: Types and Distribution

1.8K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
1.8K
The Extracellular Matrix01:42

The Extracellular Matrix

89.3K
Overview
89.3K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

572
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
572
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

279
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
279
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

336
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
336

You might also read

Related Articles

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

Sort by
Same author

Automated detection of overlapping and well-separated nanoparticles in transmission electron microscopy via deep learning.

Micron (Oxford, England : 1993)·2026
Same author

Impact of online social capital on academic performance: exploring the mediating role of online knowledge sharing.

Education and information technologies·2022
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

549

Data-driven distributed EMPC for economic optimization of interconnected systems: A Hankel matrix approach.

Fatemeh Ostovar1, Ali Akbar Safavi1, Leonhard Urbas2

  • 1Department of Power & Control Engineering, Shiraz University, Shiraz, Iran.

ISA Transactions
|February 8, 2026
PubMed
Summary

This study introduces a new data-driven distributed economic model predictive control (EMPC) method for complex systems. It improves energy efficiency and stability using only input-output data, outperforming other model predictive control approaches.

Keywords:
Consistency ConstraintData-Driven ControlDynamically Coupled SystemEconomic MPCEnergy SystemNon-Iterative Distributed Control

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
11:51

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

Published on: March 2, 2011

15.7K

Related Experiment Videos

Last Updated: Feb 10, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

549
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
11:51

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

Published on: March 2, 2011

15.7K

Area of Science:

  • Control Systems Engineering
  • Distributed Optimization
  • Data-Driven Modeling

Background:

  • Distributed systems require efficient control strategies for optimizing economic objectives.
  • Traditional model predictive control (MPC) can be computationally intensive for distributed systems.
  • Data-driven approaches offer potential for simplifying control design.

Purpose of the Study:

  • To propose a novel non-iterative, data-driven distributed economic model predictive control (EMPC) scheme.
  • To enable subsystems to optimize economic objectives using local input-output data.
  • To ensure recursive feasibility and closed-loop stability in distributed systems.

Main Methods:

  • Developed a non-iterative, data-driven EMPC framework for linear time-invariant systems.
  • Utilized input-output data for local optimization within subsystems.
  • Incorporated consistency constraints derived from Hankel matrices for neighbor interactions.
  • Designed terminal ingredients using input-output trajectories for stability guarantees.

Main Results:

  • Achieved strong duality and dissipativity with a general supply rate in a data-driven framework.
  • Demonstrated recursive feasibility and closed-loop stability through theoretical analysis.
  • Validated the method's energy efficiency and effectiveness via simulations.

Conclusions:

  • The proposed data-driven distributed EMPC scheme is effective for optimizing economic objectives in distributed systems.
  • The method offers significant advantages in energy efficiency and stability compared to existing MPC approaches.
  • This approach provides a computationally tractable solution for complex distributed control problems.