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

Optimal Arousal Theory01:23

Optimal Arousal Theory

The optimal arousal theory suggests that performance is maximized when an individual experiences a moderate level of arousal. This theory is closely tied to the Yerkes-Dodson law, which illustrates an inverted U-shaped relationship between arousal and performance. The law, formulated by psychologists Robert Yerkes and John Dodson, implies an ideal arousal level for optimal performance, and deviations from this level can lead to declines in effectiveness.
Inverted U-Shaped Performance Curve
The...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

You might also read

Related Articles

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

Sort by
Same author

Neutrophil extracellular traps drive local tumor progression and metastasis following thermal ablation in liver cancer via TLR9-mediated inflammatory feedback and immunosuppression.

Journal for immunotherapy of cancer·2026
Same author

Cuproptosis-Related Genes in Immune Infiltration and Diagnosis in Hepatitis B Virus-Related Acute Liver Failure.

Exploration (Beijing, China)·2026
Same author

MYOD1 mutation drives cancer stem cell pathways and therapy-resistance in spindle cell/sclerosing rhabdomyosarcoma.

Nature communications·2026
Same author

Fish-Scale-Inspired Giant Piezocapacitive Sensors for Human-Level Touch Perception.

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

Artificial Intelligence-Driven Sensing Framework with Multimodal Sensor Importance Learning for Smart Energy Systems.

Sensors (Basel, Switzerland)·2026
Same author

HPRT-YOLO: Enhanced lightweight UAV maritime search and rescue model based on YOLOv11n.

iScience·2026
Same journal

Prescribed-time event-triggered resilient containment control for multiagent systems against DoS attacks and disturbances.

ISA transactions·2026
Same journal

Incremental learning with prototype calibration and dynamic proxy for wind turbine fault diagnosis under time-varying operating conditions.

ISA transactions·2026
Same journal

Optimization of mode discerning control for nonlinear hybrid systems subject to unknown inputs with applications to active fault diagnosis.

ISA transactions·2026
Same journal

Convergence evaluation of optimization-based stochastic iterative learning control.

ISA transactions·2026
Same journal

Adaptive utility-aware event-triggered reinforcement learning for hybrid attack scheduling against remote state estimation.

ISA transactions·2026
Same journal

Hybrid vehicle state estimation using closed-form liquid neural networks and nonlinear Kalman filtering.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Event-triggered optimal consensus for multi-agent systems: A prescribed time optimal scheme based on DRL.

Yueyang Wang1, Fazhan Tao2, Zhumu Fu3

  • 1School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, Henan, China; Collaborative Innovation Center of Industrial Internet, Henan Province, Henan University of Science and Technology, Luoyang, 471023, Henan, China.

ISA Transactions
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an event-triggered control scheme for multi-agent systems, optimizing performance, energy, and communication. The deep reinforcement learning approach ensures prescribed-time consensus, enhancing system efficiency.

Keywords:
Actor-critic iterative learningEvent-triggered communication mechanismMulti-agent systemsOptimal consensus control

Related Experiment Videos

Last Updated: Jun 18, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Networked Systems

Background:

  • Multi-agent systems face challenges in optimizing transient performance, energy consumption, and communication resources simultaneously.
  • Existing control strategies often struggle to balance these competing objectives effectively.

Purpose of the Study:

  • To propose an event-triggered prescribed-time optimal consensus control scheme for multi-agent systems.
  • To co-optimize transient performance, energy consumption, and communication resource utilization.

Main Methods:

  • A distributed event-triggered communication mechanism with bandwidth sensing capabilities was designed.
  • An optimal consensus control protocol was developed using actor-critic neural networks and Bellman optimality theory.
  • A time-varying gain scaling function reformulated the Hamilton-Jacobi-Bellman equation for prescribed-time convergence.

Main Results:

  • The proposed scheme achieves elastic adjustment of communication load.
  • An optimal consensus controller guaranteeing prescribed-time convergence was derived.
  • The controller effectively balances performance, energy, and communication resources.

Conclusions:

  • The developed event-triggered control scheme successfully achieves optimal consensus in multi-agent systems.
  • The approach validates the effectiveness of deep reinforcement learning and prescribed-time control for complex system optimization.
  • Numerical simulations confirm the scheme's efficacy in enhancing multi-agent system performance and resource management.