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 PI Control01:27

Time and frequency -Domain Interpretation of PI Control

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

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...
Line Protection with Impedance Relays01:27

Line Protection with Impedance Relays

Coordinating time-delay overcurrent relays in complex radial systems and directional overcurrent relays in multi-source transmission loops can be challenging. Impedance relays address these issues by responding to the voltage-to-current ratio, specifically measuring the apparent impedance of a line. These relays become more sensitive during faults as current increases and voltage decreases, thereby reducing the apparent impedance.
Under normal conditions, low load currents keep the measured...
Time and frequency -Domain Interpretation of Phase-lead Control01:24

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 Controllers01:22

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...
PI Controller: Design01:24

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...

You might also read

Related Articles

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

Sort by
Same author

Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale study.

NPJ digital medicine·2026
Same author

The joint and interaction associations of physical activity and sleep with depression: a cohort study.

BMC public health·2026
Same author

Optimal weighted envelope spectrum with informative multi-band selection for bearing fault diagnosis.

ISA transactions·2026
Same author

Associations of single and mixed air pollution exposure with lung function and incident pulmonary fibrosis: The mediation effect of low-grade inflammation.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Studies on allergic diseases and B cells in the past 20 years: a bibliometric analysis via CiteSpace and VOSviewer.

Frontiers in immunology·2026
Same author

Rewiring the immune circuit: programming tumor-draining lymph node immunity with nanotechnology.

Journal of nanobiotechnology·2026
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2026

Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators
11:44

Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators

Published on: August 15, 2014

Event-Triggered Predefined-Time-Synchronized Model Predictive Selective Impedance Control.

Junyuan Xue, Wenyu Liang, Yilan Xu

    IEEE Transactions on Cybernetics
    |June 4, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces EMSIC, a control framework integrating model predictive control (MPC) and impedance control (IC). EMSIC dynamically selects impedance models for safe and efficient robotic control, achieving predefined-time convergence.

    Related Experiment Videos

    Last Updated: Jun 6, 2026

    Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators
    11:44

    Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators

    Published on: August 15, 2014

    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Model Predictive Control (MPC) and Impedance Control (IC) integration offers enhanced robotic control.
    • Existing methods face challenges with conservative or aggressive impedance models, impacting convergence speed and safety.

    Purpose of the Study:

    • To develop a novel control framework (EMSIC) that dynamically selects impedance models for improved robotic interaction.
    • To achieve predefined-time (PdT) convergence and PdT-synchronized convergence for multi-state systems.

    Main Methods:

    • Developed the EMSIC framework, enabling dynamic selection between task-preferred and conservative impedance models based on safety conditions.
    • Modified the framework for predefined-time (PdT) convergence and synchronized convergence in multi-state systems.
    • Conducted comprehensive physical robotic interaction experiments to validate the framework's performance.

    Main Results:

    • EMSIC demonstrated effective dynamic impedance model selection for safe and efficient robotic control.
    • The framework successfully achieved predefined-time (PdT) convergence, meeting user-defined time constraints.
    • Experimental validation confirmed the practical effectiveness and performance of the proposed control framework.

    Conclusions:

    • The EMSIC framework offers a robust solution for integrating MPC and IC, addressing limitations of existing approaches.
    • EMSIC enhances robotic interaction safety and control performance through adaptive impedance modeling.
    • The proposed control strategy shows significant potential for advanced robotic applications requiring precise and safe interactions.