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

First Order Systems

First Order Systems

First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...

Second Order systems I

Second Order systems I

A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...

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.
If ζ...

Partial Fractions

Partial Fractions

A partial fraction is a component of a rational expression represented as the sum of simpler fractions. When a rational function is expressed as a ratio of two polynomials, it can often be decomposed into a sum of fractions whose denominators are simpler polynomials, typically linear or irreducible quadratic factors. This process is called partial fraction decomposition, and it is used to simplify complex expressions for integration, solving equations, or analysis.Partial fraction decomposition...

State Space Representation

State Space Representation

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...

Feedback control systems

Feedback control systems

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...

You might also read

Related Articles

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

Sort by

Same author

Vertical ferroelectric switching by in-plane sliding of two-dimensional bilayer WTe<sub>2</sub>.

Nanoscale·2019

Same author

Loss of control over mild aversive events produces significant helplessness in mice.

Behavioural brain research·2019

Same author

Characterization of an Alkaline GH49 Dextranase from Marine Bacterium <i>Arthrobacter</i> <i>oxydans</i> KQ11 and Its Application in the Preparation of Isomalto-Oligosaccharide.

Marine drugs·2019

Same author

Spin-lattice correlation in Eu<sup>3+</sup> doped antiferromagnet TmFeO<sub>3</sub>.

Physical chemistry chemical physics : PCCP·2019

Same author

Characterization and mechanism of the effects of Mg-Fe layered double hydroxide nanoparticles on a marine bacterium: new insights from genomic and transcriptional analyses.

Biotechnology for biofuels·2019

Same author

Observer-Based Distributed Mean-Square Consensus Design for Leader-Following Multiagent Markov Jump Systems.

IEEE transactions on cybernetics·2019

Search research articles

Related Experiment Video

Updated: Jun 21, 2026

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

Distributed coordination of networked fractional-order systems.

Yongcan Cao¹, Yan Li, Wei Ren

¹Department of Electrical and Computer Engineering, Utah State University, Logan, UT 84322 USA. yongcan.cao@aggiemail.usu.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|July 11, 2009

Summary

This summary is machine-generated.

This study explores distributed coordination in networked fractional-order systems. Fractional-order coordination algorithms can achieve faster convergence than integer-order ones, especially when fractional orders are time-varying.

Related Experiment Videos

Last Updated: Jun 21, 2026

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

Area of Science:

Control Systems Engineering
Networked Systems Theory
Fractional Calculus Applications

Background:

Distributed coordination is crucial for networked systems.
Fractional-order systems offer enhanced dynamics compared to traditional integer-order systems.
Understanding coordination in fractional-order networks is an emerging research area.

Purpose of the Study:

To introduce a general fractional-order coordination model for networked systems.
To establish conditions for achieving coordination in these systems.
To analyze and compare the convergence properties of fractional-order versus integer-order coordination.

Main Methods:

Development of a generalized fractional-order coordination model.
Analysis of system dynamics under directed interaction graphs.
Derivation of sufficient conditions for coordination based on graph properties and fractional order.
Explicitly defining the coordination equilibrium.
Comparative analysis of convergence rates.

Main Results:

Sufficient conditions for achieving distributed coordination in fractional-order systems are presented.
The coordination equilibrium for the general model is explicitly determined.
A relationship between the number of agents, fractional order, and coordination is established.
Fractional-order systems demonstrate potentially faster convergence than integer-order systems.
Time-varying fractional orders can further enhance convergence speed.

Conclusions:

The proposed general fractional-order coordination model effectively addresses distributed coordination in networked systems.
Coordination is achievable under specific conditions related to the interaction graph and fractional order.
Fractional-order coordination offers advantages in convergence speed, which can be optimized through dynamic order adjustment.