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

Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

155
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...
155
Multimachine Stability01:25

Multimachine Stability

240
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
240
Control System Problem01:21

Control System Problem

195
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...
195
Feedback control systems01:26

Feedback control systems

462
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...
462
Transient and Steady-state Response01:24

Transient and Steady-state Response

291
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...
291
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

211
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
211

You might also read

Related Articles

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

Sort by
Same author

Robust Pose and Inertial Parameter Estimation of an Unknown Aircraft Based on Variational Bayesian Dual Vector Quaternion Extended Kalman Filter.

Entropy (Basel, Switzerland)·2026
Same author

Bounded-acceleration impact time control guidance based on look-angle shaping.

ISA transactions·2026
Same author

The host genes influencing <i>Clostridioides difficile</i> infection and the potential role of intestinal Lactobacillus acidophilus: a Mendelian randomization and animal model study.

Frontiers in cellular and infection microbiology·2025
Same author

A bibliometric study of global trends in T1DM and intestinal flora research.

Frontiers in microbiology·2024
Same author

Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network.

Sensors (Basel, Switzerland)·2024
Same author

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network.

Entropy (Basel, Switzerland)·2024

Related Experiment Video

Updated: Sep 27, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable.

Hanqiao Huang1, Haoyu Cheng1, Ruijia Song2

  • 1Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, China.

Computational Intelligence and Neuroscience
|April 14, 2022
PubMed
Summary

This study introduces an intelligent switched fault detection filter using mode-dependent average dwell time (MDADT) and deep reinforcement learning for unstable switched systems. The novel filter ensures system stability and performance, validated by simulations.

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.2K

Related Experiment Videos

Last Updated: Sep 27, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.2K

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Fault Detection and Diagnosis

Background:

  • Switched systems with unstable subsystems pose significant challenges for fault detection.
  • Traditional fault detection filters often struggle with complex system dynamics and transient performance.

Purpose of the Study:

  • To design an intelligent switched fault detection filter for unstable switched systems.
  • To enhance filter performance using a hybrid approach combining dynamics-based and learning-based methods.
  • To guarantee system stability and prescribed attenuation performance.

Main Methods:

  • Mode-dependent average dwell time (MDADT) for generating time-dependent switching signals.
  • A hybrid filter combining dynamics-based (using linear matrix inequalities) and learning-based (deep reinforcement learning, actor-critic) components.
  • Multiple Lyapunov function (MLF) method for stability and performance guarantees.
  • Deep deterministic policy gradient algorithm and nonfragile control for robustness.

Main Results:

  • The proposed switched fault detection filter effectively generates residual signals.
  • Stability and prescribed attenuation performance are guaranteed by MDADT and MLF methods.
  • Deep reinforcement learning improves the transient performance of the filter.
  • Simulation results demonstrate the effectiveness of the proposed intelligent fault detection method.

Conclusions:

  • The developed intelligent switched fault detection filter is effective for systems with unstable subsystems.
  • The hybrid filter design, incorporating deep reinforcement learning, enhances fault detection capabilities.
  • The method provides a robust solution for fault detection in complex switched systems.