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

Network Function of a Circuit01:25

Network Function of a Circuit

1.1K
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
1.1K
Control Systems01:10

Control Systems

1.7K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.7K
Open and closed-loop control systems01:17

Open and closed-loop control systems

2.0K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
2.0K
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

2.0K
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
2.0K
Control System Problem01:21

Control System Problem

578
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
578
Control of Power Flow01:30

Control of Power Flow

865
There are several methods to control power flow in power systems:
865

You might also read

Related Articles

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

Sort by
Same author

Intrapartum caesarean delivery and childhood BMI trajectories in relation to the infant gut microbiome in the VDAART prospective birth cohort.

EBioMedicine·2026
Same author

Network-driven discovery of repurposable drugs targeting hallmarks of aging.

Nature aging·2026
Same author

Optimal dismantling of directed networks.

Nature communications·2026
Same author

The aging genome exhibits organized vulnerability to somatic mutations.

bioRxiv : the preprint server for biology·2026
Same author

Machine learning-based Personalized Dietary Recommendations to Achieve Desired Gut Microbial Compositions.

bioRxiv : the preprint server for biology·2026
Same author

Association of Probable Post-Traumatic Stress Disorder with Dietary Pattern and Gut Microbiome in a Cohort of Women.

Nature. Mental health·2026
Same journal

Retraction Note: NSD2 targeting reverses plasticity and drug resistance in prostate cancer.

Nature·2026
Same journal

Enhanced B cell priming induces broadly neutralizing HIV-1 apex antibodies.

Nature·2026
Same journal

Vaccination elicits HIV broadly neutralizing antibodies in primates.

Nature·2026
Same journal

Child online safety needs more than social-media bans.

Nature·2026
Same journal

Ebola preparedness must start with ecosystems and before humans show symptoms.

Nature·2026
Same journal

AI tools can speed up thinking, but evidence still comes from the lab bench.

Nature·2026
See all related articles

Related Experiment Video

Updated: Apr 30, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.3K

Controllability of complex networks.

Yang-Yu Liu1, Jean-Jacques Slotine, Albert-László Barabási

  • 1Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA.

Nature
|May 13, 2011
PubMed
Summary
This summary is machine-generated.

Controlling complex systems requires identifying specific driver nodes. These nodes, crucial for system dynamics, surprisingly avoid high-influence hubs in networks, aiding in control strategies.

More Related Videos

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories
04:15

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

Published on: February 23, 2024

1.9K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

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

Published on: February 14, 2025

1.1K

Related Experiment Videos

Last Updated: Apr 30, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.3K
Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories
04:15

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

Published on: February 23, 2024

1.9K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

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

Published on: February 14, 2025

1.1K

Area of Science:

  • Complex systems science
  • Network theory
  • Control theory

Background:

  • Understanding and controlling complex systems is key to advancing science and technology.
  • Existing control theory lacks a framework for complex, self-organized systems.
  • Controllability analysis is essential for managing emergent behaviors in networks.

Purpose of the Study:

  • To develop analytical tools for assessing the controllability of complex directed networks.
  • To identify the minimal set of driver nodes required to control system dynamics.
  • To investigate the relationship between network structure and the number of driver nodes.

Main Methods:

  • Development of analytical tools to study network controllability.
  • Identification of driver nodes essential for system-wide control.
  • Application of tools to diverse real-world and model networks.

Main Results:

  • The number of driver nodes is primarily dictated by the network's degree distribution.
  • Sparse, inhomogeneous networks are the most challenging to control.
  • Dense, homogeneous networks are controllable with few driver nodes.
  • Driver nodes in real and model systems tend to avoid high-degree nodes.

Conclusions:

  • A novel framework for analyzing the controllability of complex networks has been established.
  • Network structure significantly influences the ease of system control.
  • Targeting specific, often low-degree, driver nodes offers an efficient control strategy.