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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

274
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
274
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
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...
96
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

79
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...
79
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

566
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
566
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

432
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
432
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

127
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...
127

You might also read

Related Articles

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

Sort by
Same author

SynTME: A tumor microenvironment-aware, pharmacology-inspired multi-stage framework for drug synergy prediction.

Computer methods and programs in biomedicine·2026
Same author

Enhanced nitrogen and phosphorus removal from mining-affected waters by micro-nano aeration coupled with microbial remediation.

Environmental technology·2026
Same author

Approximate Synchronization in Distribution of Coupled Probabilistic Boolean Networks.

IEEE transactions on cybernetics·2026
Same author

Comparative analysis of four nutritional scores in predicting hospital stay duration for EICU Patients with acute pancreatitis.

Frontiers in nutrition·2026
Same author

A TaKNOX1-TaAPO1-Rht1 feedback regulatory module orchestrates spikelet number and yield potential in wheat.

Plant communications·2026
Same author

In Situ Construction of Superhydrophobic Photothermal Coatings Based on Metal-Polyphenol Coordination Complex for Anti-/De-Icing Applications.

Polymers·2026

Related Experiment Video

Updated: Jul 16, 2025

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells
06:48

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Published on: January 5, 2024

3.7K

Sampled-data exponential consensus of multi-agent systems with Lipschitz nonlinearities.

Wenqing Zhao1, Guoliang Chen1, Xiangpeng Xie2

  • 1School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 20, 2023
PubMed
Summary

This study presents a new aperiodic sampled-data control method for multi-agent systems, achieving exponential consensus with improved stability and resource efficiency. The approach enhances communication bandwidth and reduces resource usage in networked systems.

Keywords:
ConsensusLMIMulti-agent systemsSampled-data

More Related Videos

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.4K
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

5.0K

Related Experiment Videos

Last Updated: Jul 16, 2025

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells
06:48

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Published on: January 5, 2024

3.7K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.4K
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

5.0K

Area of Science:

  • Control Systems Engineering
  • Networked Systems
  • Nonlinear Dynamics

Background:

  • Multi-agent systems (MAS) require robust control strategies for coordinated behavior.
  • Lipschitz nonlinear dynamics present challenges in achieving consensus.
  • Sampled-data control introduces complexities due to discrete information updates.

Purpose of the Study:

  • To develop an aperiodic sampled-data control method for exponential consensus in MAS with Lipschitz nonlinear dynamics.
  • To enhance stability conditions and computational efficiency.
  • To conserve communication bandwidth and reduce resource consumption.

Main Methods:

  • Reformulating the sampled-data system as a continuous system with time-varying input delay.
  • Constructing a two-sided loop-based Lyapunov functional (LBLF) to analyze sampled-data patterns.
  • Utilizing Laplacian matrix symmetry and Newton-Leibniz formula for reduced LMI dimensions.

Main Results:

  • Achieved exponential consensus for leaderless and leader-following MAS.
  • Designed an aperiodic sampled-data controller simplifying stability analysis.
  • Demonstrated a larger achievable sampled-data interval compared to existing literature.

Conclusions:

  • The proposed method effectively achieves exponential consensus under aperiodic sampled-data control.
  • The approach offers significant advantages in terms of stability, computation, and resource efficiency.
  • This technique is applicable to complex systems, including power systems, with potential for broader adoption.