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

Feedback control systems01:26

Feedback control systems

347
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...
347
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

141
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
141
Classification of Systems-I01:26

Classification of Systems-I

215
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
215
Mechanical Systems01:22

Mechanical Systems

234
Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
234
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

129
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
129
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

439
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....
439

You might also read

Related Articles

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

Sort by
Same author

Hypernetworks induce stable hyperlocking.

Nature communications·2026
Same author

SmartTrap: automated precision experiments with optical tweezers.

Nature methods·2026
Same author

Growth rate-dependent mutations and phenotypic switching facilitate rapid adaptation.

NPJ systems biology and applications·2026
Same author

Community structure-regulation coupling reveals optimal information diffusion.

Nature communications·2026
Same author

Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.

Nature communications·2026
Same author

Modeling the influence of interactions on different variables in a turbulent thermoacoustic system.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026
Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026
Same journal

Data-driven soliton manifold approximations for dark and bright waves: Some prototypical 1D case examples.

Chaos (Woodbury, N.Y.)·2026
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jul 22, 2025

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

4.4K

Perspectives on adaptive dynamical systems.

Jakub Sawicki1,2, Rico Berner3, Sarah A M Loos4

  • 1Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany.

Chaos (Woodbury, N.Y.)
|July 24, 2023
PubMed
Summary
This summary is machine-generated.

Adaptivity, a key feature in nature and technology, involves systems that change and adjust. This interdisciplinary review explores adaptivity across fields, identifying challenges and future research directions.

More Related Videos

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

5.0K
Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.0K

Related Experiment Videos

Last Updated: Jul 22, 2025

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

4.4K
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

5.0K
Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.0K

Area of Science:

  • Complex Systems Science
  • Interdisciplinary Studies

Background:

  • Adaptivity is a fundamental dynamical feature observed across natural, socio-economic, and technological systems.
  • Adaptive couplings are crucial in real-world networks like power grids, social networks, and neural networks.
  • These adaptive mechanisms underpin closed-loop control strategies and machine learning algorithms.

Purpose of the Study:

  • To provide an interdisciplinary perspective on adaptive systems.
  • To examine the concept and terminology of adaptivity across diverse scientific disciplines.
  • To identify common challenges and outline future research directions in the study of adaptivity.

Main Methods:

  • Literature review and synthesis of adaptivity concepts from various fields.
  • Comparative analysis of adaptivity's role and manifestations in different domains.
  • Identification of cross-disciplinary commonalities and divergences in adaptivity research.

Main Results:

  • Adaptivity plays a critical role in system stability, resilience, and functionality across numerous disciplines.
  • A unified understanding of adaptivity is hindered by discipline-specific terminology and focus.
  • Significant opportunities exist for interdisciplinary collaboration to advance the understanding of adaptive systems.

Conclusions:

  • Understanding adaptivity requires an interdisciplinary approach, integrating insights from diverse fields.
  • Addressing common challenges in adaptivity research can foster innovation and new methodologies.
  • Future research should focus on developing unified frameworks and exploring novel applications of adaptive systems.