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

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

491
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....
491
Transient and Steady-state Response01:24

Transient and Steady-state Response

248
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
248
State Space Representation01:27

State Space Representation

265
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
265
State Space to Transfer Function01:21

State Space to Transfer Function

287
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
287
Multimachine Stability01:25

Multimachine Stability

216
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:
216
Feedback control systems01:26

Feedback control systems

382
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
382

You might also read

Related Articles

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

Sort by
Same author

Output Tracking of Periodically Time-Varying Boolean Networks: State-Flipped Control and Q-Learning Approaches.

IEEE transactions on neural networks and learning systems·2026
Same author

Dual-stream feature-decoupling network for rapid and robust rice traceability using handheld Raman spectroscopy.

Analytica chimica acta·2026
Same author

Potential impacts of delay on pinning impulsive secure synchronization control of delayed networks.

ISA transactions·2025
Same author

Learning-based minimum cost strategies for set reachability of Boolean control networks under data injection attacks.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

An approach to inferring gene regulatory networks via boolean modeling and feature selection.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Impulsive Observer of Linear Systems: An Adaptive Impulsive Gain Approach.

IEEE transactions on cybernetics·2025

Related Experiment Video

Updated: Aug 24, 2025

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.1K

State-Feedback Set Stabilization of Boolean Networks With State-Dependent Random Impulses.

Yang Wang, Xueying Ding, Lin Yang

    IEEE Transactions on Cybernetics
    |October 20, 2022
    PubMed
    Summary

    This study introduces a new hybrid model for Boolean control networks with random impulses, enabling clearer modeling of impulsive behaviors and achieving finite-time and asymptotic set stabilization.

    More Related Videos

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.4K
    State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
    12:38

    State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

    Published on: December 28, 2010

    10.6K

    Related Experiment Videos

    Last Updated: Aug 24, 2025

    Designing and Implementing Nervous System Simulations on LEGO Robots
    10:34

    Designing and Implementing Nervous System Simulations on LEGO Robots

    Published on: May 25, 2013

    15.1K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.4K
    State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
    12:38

    State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

    Published on: December 28, 2010

    10.6K

    Area of Science:

    • Control Theory
    • Discrete Mathematics
    • Network Science

    Background:

    • Boolean control networks (BCNs) are widely used to model complex systems.
    • Impulsive behaviors in BCNs require advanced modeling techniques.
    • Existing models may not fully capture the nuances of state-dependent random impulses.

    Purpose of the Study:

    • To develop a novel hybrid index model for Boolean control networks with state-dependent random impulses.
    • To establish criteria for finite-time and asymptotic set stabilization.
    • To design effective set stabilizers for such networks.

    Main Methods:

    • Introduction of a hybrid index model for improved representation of impulsive dynamics.
    • Development of the concept of forward completeness to prevent Zeno phenomena.
    • Algorithm for deriving control invariant subsets.
    • Derivation of necessary and sufficient conditions for set stabilizability.

    Main Results:

    • A novel framework for state-feedback set stabilization of BCNs with state-dependent random impulses.
    • Criteria for finite-time and asymptotic set stabilizability.
    • Design of layered asymptotic set stabilizers.
    • Investigation into the relationships between different stabilizability types.

    Conclusions:

    • The proposed hybrid index model effectively addresses state-dependent random impulses in BCNs.
    • The developed criteria and algorithms ensure robust set stabilization.
    • The study provides a comprehensive approach to analyzing and controlling complex impulsive systems.