Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Multimachine Stability

Multimachine Stability

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:

Second Order systems II

Second Order systems II

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.

Transient and Steady-state Response

Transient and Steady-state Response

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...

Stability

Stability

The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...

Pole and System Stability

Pole and System Stability

The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...

You might also read

Related Articles

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

Sort by

Same author

Inverse Optimal Control in Conjunction With Inverse Reinforcement Learning for Distributed Parameter Systems.

IEEE transactions on cybernetics·2026

Same author

An Event-Triggered Decentralized Asynchronous Design Scheme for Positive Interconnected Switched Systems With MDMDT Switching.

IEEE transactions on cybernetics·2025

Same author

Variable Threshold-Oriented Event-Triggered Cluster Consensus for Groups of Multiagent Systems.

IEEE transactions on cybernetics·2025

Same author

Space-Confined Synthesis of Sulfonated Covalent Organic Framework‒Polymer Membranes for Enhanced Osmotic Energy Conversion.

Small (Weinheim an der Bergstrasse, Germany)·2025

Same author

Preparation and Performance Evaluation of Coated Tracer Sand for Segmental Fracturing of Horizontal Wells.

ACS omega·2025

Same author

Quantum dynamics in multistate harmonic models using tensor-train thermofield dynamics and semiclassical mapping dynamics.

The Journal of chemical physics·2025

Search research articles

Related Experiment Video

Updated: Jul 7, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Event-Based Two-Step Transmission Mechanism for the Stabilization of Networked T-S Fuzzy Systems With Random

Zhou Gu, Yujian Fan, Xiang Sun

IEEE Transactions on Cybernetics

|December 22, 2023

Summary

This study introduces a novel two-step transmission mechanism (TSTM) for networked fuzzy control systems. This method efficiently reduces network bandwidth by intelligently discarding redundant data packets, enhancing system stability and performance.

More Related Videos

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Related Experiment Videos

Last Updated: Jul 7, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

Control Systems Engineering
Networked Fuzzy Systems
Information Theory

Background:

Traditional event-triggered mechanisms (ETM) in networked control systems often struggle to identify and discard redundant data packets effectively.
This inefficiency leads to unnecessary network bandwidth consumption, particularly in systems approaching stability.
Existing methods lack robust strategies for managing data transmission in fuzzy-based nonlinear systems with uncertainties.

Purpose of the Study:

To propose and analyze an event-based two-step transmission mechanism (TSTM) for networked Takagi-Sugeno (T-S) fuzzy systems.
To reduce network bandwidth load by optimizing data packet transmission.
To develop a control strategy for T-S fuzzy nonlinear systems with random uncertainties and ensure stability.

Main Methods:

A two-step transmission mechanism (TSTM) is implemented: Step 1 uses a traditional event-triggered mechanism (ETM) for initial packet relabeling.
Step 2 employs a probabilistic approach to identify real release packets (RRP), discarding redundant ones.
A novel timing analysis technique is proposed to derive conditions for mean-square asymptotic stability (MSAS) in nonlinear systems.

Main Results:

The proposed TSTM effectively reduces network bandwidth usage by discarding unnecessary data packets, especially during system stabilization.
The developed control strategy ensures mean-square asymptotic stability (MSAS) for T-S fuzzy nonlinear systems with random uncertainties.
The effectiveness of the TSTM is validated through two practical application examples.

Conclusions:

The event-based TSTM offers a superior approach for data transmission in networked T-S fuzzy systems compared to traditional ETMs.
This mechanism significantly alleviates network bandwidth burden, enhancing overall system efficiency.
The study provides a robust framework for designing stable control systems in uncertain networked environments.