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

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
Feedback control systems01:26

Feedback control systems

794
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...
794
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

1.6K
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...
1.6K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.9K
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...
1.9K
Transient and Steady-state Response01:24

Transient and Steady-state Response

748
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...
748

You might also read

Related Articles

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

Sort by
Same author

Noncooperative Quantum Networks.

Physical review letters·2026
Same author

How heterogeneity shapes dynamics and computation in the brain.

Neuron·2025
Same author

Interpretable Disorder-Promoted Synchronization and Coherence in Coupled Laser Networks.

Physical review letters·2025
Same author

Optimal flock formation induced by agent heterogeneity.

Nature communications·2025
Same author

Grid congestion stymies climate benefit from U.S. vehicle electrification.

Nature communications·2025
Same author

Introduction to focus issue: Topics in nonlinear science.

Chaos (Woodbury, N.Y.)·2025
Same journal

Erratum: Spectroscopy and Ground-State Transfer of Ultracold Bosonic ^{39}K^{133}Cs Molecules [Phys. Rev. Lett. 135, 203401 (2025)].

Physical review letters·2026
Same journal

Erratum: Lifetime of the ^{2}F_{7/2} Level in Yb^{+} for Spontaneous Emission of Electric Octupole Radiation [Phys. Rev. Lett. 127, 213001 (2021)].

Physical review letters·2026
Same journal

Laser-Plasma Based Seeded Free Electron Laser in the High-Gain Regime.

Physical review letters·2026
Same journal

Parent Hamiltonians for Stabilizer Quantum Many-Body Scars.

Physical review letters·2026
Same journal

Properties of Heavy Cosmic Nuclei Phosphorus, Chlorine, Argon, Potassium, and Calcium: Results from the Alpha Magnetic Spectrometer.

Physical review letters·2026
Same journal

Role of Spin-Isospin Symmetries in Nuclear β-Decays.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Apr 25, 2026

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

Controllability transition and nonlocality in network control.

Jie Sun1, Adilson E Motter2

  • 1Department of Mathematics and Computer Science, Clarkson University, Potsdam, New York 13699, USA.

Physical Review Letters
|August 29, 2014
PubMed
Summary
This summary is machine-generated.

Minimizing driver nodes in network control is challenging. Numerical control often fails due to ill-conditioned Gramians, requiring more control inputs for success, not just precision.

More Related Videos

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

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

Published on: May 8, 2021

4.2K
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.3K

Related Experiment Videos

Last Updated: Apr 25, 2026

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
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

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

Published on: May 8, 2021

4.2K
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.3K

Area of Science:

  • Network science
  • Control theory
  • Computational mathematics

Background:

  • Minimizing driver nodes is a key objective in large network control.
  • Physical determination of control signals and trajectories is underexplored.

Purpose of the Study:

  • Investigate the practical limitations of numerical control in large networks.
  • Analyze the relationship between control inputs, trajectory properties, and numerical success.
  • Characterize the phenomenon of numerical controllability transition.

Main Methods:

  • Analysis of numerical control failure in linear systems.
  • Examination of control trajectory properties (nonlocality, length).
  • Correlation analysis between numerical success rate, control inputs, and Gramian condition.

Main Results:

  • Numerical control fails for ill-conditioned controllability Gramians, even with satisfied criteria.
  • Control trajectories are often nonlocal in phase space and anti-correlated with success rate.
  • A sharp increase in numerical success rate occurs with more control inputs (numerical controllability transition).

Conclusions:

  • Numerical control failure is linked to ill-conditioned Gramians and trajectory nonlocality.
  • Increasing numerical precision alone does not guarantee successful control.
  • Overcoming control failure necessitates increasing control inputs beyond the numerical controllability transition point.