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

Multimachine Stability01:25

Multimachine Stability

143
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
143
Second Order systems II01:18

Second Order systems II

93
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
93

You might also read

Related Articles

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

Sort by
Same author

Applications and advances in molecular diagnostics: revolutionizing non-tuberculous mycobacteria species and subspecies identification.

Frontiers in public health·2024
Same author

Optic nerve compression associated with visual cortex functional alteration in dysthyroid optic neuropathy: A combined orbital and brain imaging study.

CNS neuroscience & therapeutics·2024
Same author

Association between cathepsins and benign prostate diseases: a bidirectional two-sample Mendelian randomization study.

Frontiers in endocrinology·2024
Same author

Global pattern of organic carbon pools in forest soils.

Global change biology·2024
Same author

PFOS Exposure Promotes Hepatotoxicity in Quails by Exacerbating Oxidative Stress and Inflammation-Induced Apoptosis through Activating TLR4/MyD88/NF-κb Signaling.

ACS omega·2024
Same author

Warmer and drier ecosystems select for smaller bacterial genomes in global soils.

iMeta·2024

Related Experiment Video

Updated: Jun 12, 2025

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

9.9K

Exponential Asynchronous Stabilization for Delayed Semi-Markovian Neural Networks via DAEIC.

Haiyang Zhang, Jing Na, Lianglin Xiong

    IEEE Transactions on Neural Networks and Learning Systems
    |September 18, 2024
    PubMed
    Summary

    This study presents a novel discrete adaptive event-triggered impulsive control for neural networks with semi-Markov jump parameters and time-varying delays, ensuring exponential asynchronous stabilization.

    More Related Videos

    Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)
    06:44

    Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)

    Published on: January 10, 2025

    440
    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
    08:08

    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

    Published on: June 24, 2015

    11.4K

    Related Experiment Videos

    Last Updated: Jun 12, 2025

    Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
    07:41

    Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

    Published on: June 5, 2017

    9.9K
    Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)
    06:44

    Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)

    Published on: January 10, 2025

    440
    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
    08:08

    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

    Published on: June 24, 2015

    11.4K

    Area of Science:

    • Control Systems Engineering
    • Computational Neuroscience
    • Networked Systems

    Background:

    • Neural networks (NNs) with semi-Markov jump (SMJ) parameters and additive time-varying delays (ATDs) present complex stabilization challenges.
    • Distinct SMJ parameters in controller gain and system structure reflect real-world scenarios.
    • Existing control methods may impose high communication loads.

    Purpose of the Study:

    • To address the exponential asynchronous stabilization (EAS) for NNs with SMJ parameters and ATDs.
    • To propose a discrete adaptive event-triggered impulsive control (DAEIC) scheme to reduce communication load.
    • To develop a flexible looped Lyapunov-Krasovski functional (LLKF) for improved analysis.

    Main Methods:

    • A novel DAEIC scheme with an adaptive update rule (AUR) for dynamic threshold adjustment.
    • Construction of a flexible LLKF to incorporate system dynamics, delays, and control parameters.
    • Application of inequality analysis techniques combined with the LLKF and DAEIC scheme.

    Main Results:

    • Novel theoretical results guaranteeing the EAS of the considered NNs are derived.
    • The proposed DAEIC scheme effectively manages communication load while ensuring system stability.
    • The LLKF provides a comprehensive framework for analyzing systems with heterogeneous parameters and delays.

    Conclusions:

    • The developed control strategy ensures EAS for neural networks with complex parameter and delay dynamics.
    • The event-triggered approach significantly reduces communication burden.
    • Validation through three examples confirms the efficacy of the proposed methodology.