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

Time and frequency -Domain Interpretation of Phase-lag Control

Time and frequency -Domain Interpretation of Phase-lag Control

Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any finite,...

Time and frequency -Domain Interpretation of Phase-lead Control

Time and frequency -Domain Interpretation of Phase-lead Control

Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...

Phase-lead and Phase-lag Controllers

Phase-lead and Phase-lag Controllers

Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass filters, manage...

Time-Domain Interpretation of PD Control

Time-Domain Interpretation of PD Control

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...

Frequency-Domain Interpretation of PD Control

Frequency-Domain Interpretation of PD Control

Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the system's...

Linear time-invariant Systems

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...

You might also read

Related Articles

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

Sort by

Same author

Temporal dynamics of neural activity in autism: dynamical systems perspective on sensitivity, neural learning, and social interactions.

Frontiers in neuroscience·2026

Same author

Effects of T-type and L-type calcium currents on synchronized activity patterns in a model subthalamo-pallidal network.

Chaos (Woodbury, N.Y.)·2026

Same author

Multimodal antigenic escape to GPRC5D-targeted T cell engagers in multiple myeloma.

Nature medicine·2026

Same author

Plasma cell identity escape drives resistance to anti-BCMA T-cell-redirecting therapy in multiple myeloma.

bioRxiv : the preprint server for biology·2025

Same author

Timing Genomic Antigen Loss in Multiple Myeloma Treated with T Cell-Redirecting Immunotherapies.

Blood cancer discovery·2025

Same author

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2025

Search research articles

Related Experiment Video

Updated: May 29, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Detecting the temporal structure of intermittent phase locking.

Sungwoo Ahn¹, Choongseok Park, Leonid L Rubchinsky

¹Department of Mathematical Sciences, Indiana University Purdue University, Indianapolis, Indiana 46202, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|August 27, 2011

Summary

This study introduces a new method to analyze intermittent phase locking in oscillatory systems. It quantifies how synchronization is lost and regained, offering insights into complex system dynamics.

More Related Videos

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Published on: February 28, 2016

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

Related Experiment Videos

Last Updated: May 29, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Published on: February 28, 2016

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

Area of Science:

Nonlinear Dynamics
Complex Systems Analysis
Statistical Physics

Background:

Oscillatory systems often exhibit intermittent phase locking, a state where synchronization is temporarily lost and regained.
Traditional methods focusing on stable synchronized states do not fully capture the dynamics of these intermittent regimes.
Understanding the temporal structure of synchronization and desynchronization is crucial for characterizing complex system behavior.

Purpose of the Study:

To develop and validate a novel method for characterizing the temporal dynamics of intermittent phase locking.
To provide quantitative measures for the loss and recovery of phase locking in weakly synchronized systems.
To differentiate between systems with similar overall synchronization levels but distinct temporal synchronization structures.

Main Methods:

Analyzing the relationship between phases across successive oscillation cycles.
Quantifying the duration and frequency of desynchronized episodes.
Investigating the return dynamics to a phase-locked state.
Applying the method to diverse systems: coupled skewed tent maps, Rössler and Lorenz oscillators, and coupled neurons.

Main Results:

The proposed method effectively characterizes the temporal structure of intermittent phase locking.
It successfully distinguishes between systems with similar overall phase locking and stability but different synchronization dynamics.
The analysis reveals distinct patterns in synchronization and desynchronization events across different model systems.

Conclusions:

The developed approach offers a robust way to analyze intermittent phase locking beyond traditional stability measures.
This method provides valuable insights into the complex temporal dynamics of weakly synchronized systems.
It is applicable to a range of systems, from simple mathematical models to more complex biological networks.