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

Pole and System Stability01:24

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 response.
Stability01:28

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...
Control System Problem01:21

Control System Problem

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...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Feedback control systems01:26

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...
Root-Locus Method01:19

Root-Locus Method

A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block diagram,...

You might also read

Related Articles

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

Sort by
Same author

Adapting Temperature Predictions to MR Imaging in Treatment Position to Improve Simulation-Guided Hyperthermia for Cervical Cancer.

IEEE open journal of engineering in medicine and biology·2024
Same author

BODY COMPOSITION CHANGES IN MEN WITH HIV/HCV COINFECTION, HIV MONOINFECTION, AND HCV MONOINFECTION.

Acta endocrinologica (Bucharest, Romania : 2005)·2023
Same author

Integrated thermal and magnetic susceptibility modeling for air-motion artifact correction in proton resonance frequency shift thermometry.

International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group·2022
Same author

POD-Kalman filtering for improving noninvasive 3D temperature monitoring in MR-guided hyperthermia.

Medical physics·2022
Same author

Visualization of thermal washout due to spatiotemporally heterogenous perfusion in the application of a model-based control algorithm for MR-HIFU mediated hyperthermia.

International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group·2021
Same author

BONE QUALITY IN A YOUNG COHORT OF HIV-POSITIVE PATIENTS.

Acta endocrinologica (Bucharest, Romania : 2005)·2020

Related Experiment Video

Updated: Jun 8, 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

Stability analysis and controller synthesis for hybrid dynamical systems.

W P M H Heemels1, B De Schutter, J Lunze

  • 1Hybrid and Networked Systems Group, Department of Mechanical Engineering, Eindhoven University of Technology, PO Box 513, NL-5600 MB Eindhoven, The Netherlands. m.heemels@tue.nl

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|October 6, 2010
PubMed
Summary

Hybrid systems integrate continuous and discrete dynamics, crucial for modeling complex technological, biological, and economic systems. This research surveys hybrid systems theory, focusing on modeling, analysis, and control design from a control community perspective.

Related Experiment Videos

Last Updated: Jun 8, 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

Area of Science:

  • Control Engineering and Systems Theory
  • Applied Mathematics
  • Computer Science

Background:

  • Hybrid systems emerge where continuous and discrete dynamics interact, common in technological systems combining logic and control with physical processes.
  • Accurate modeling of mechanical, biological, electrical, and economic systems often necessitates hybrid models.
  • These systems require mathematical frameworks that integrate continuous dynamics (differential/difference equations) with discrete-event dynamics (automata).

Purpose of the Study:

  • To provide an overview of hybrid dynamical systems from the perspective of the control community.
  • To survey major research lines in modeling, analysis, and control design for hybrid systems.
  • To highlight the importance of hybrid systems theory in the multidisciplinary design of modern technological systems.

Main Methods:

  • Review of mathematical modeling techniques combining differential/difference equations with automata or discrete-event models.
  • Analysis of existing hybrid systems theory, encompassing both continuous and discrete aspects.
  • Survey of control design methodologies applicable to hybrid dynamical systems.

Main Results:

  • Identification of hybrid systems as essential for describing systems with interacting continuous and discrete dynamics.
  • Characterization of hybrid models as combinations of continuous and discrete mathematical formalisms.
  • Emphasis on the significant role of hybrid systems theory in the multidisciplinary design of complex systems.

Conclusions:

  • Hybrid systems theory is a vital and active research area crucial for understanding and controlling complex modern systems.
  • The integration of continuous and discrete dynamics modeling is key to advancing control engineering.
  • Further research in analysis and control design for hybrid systems will continue to impact technological advancements.