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

Second Order systems II01:18

Second Order systems II

459
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
459
Second Order systems I01:20

Second Order systems I

699
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
699
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

457
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 of...
457
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.9K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.9K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.6K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

387
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
387

You might also read

Related Articles

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

Sort by
Same author

Dual Action of Phase Separation and Mechanical Locking Enabled Low-Value Waste Wood Into High-Performance Structural Phase-Change-Induced Self-Healing Materials.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

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

IEEE transactions on cybernetics·2026
Same author

Adaptive Safe Control for Bounded Rational Human-Robot Interaction Based on Reinforcement Learning and Reachability Analysis.

IEEE transactions on cybernetics·2026
Same author

Stability Switching and Oscillation Regulation Strategies for Large-Scale Fractional-Order Neural Networks With Double Hubs and Multiple Delays.

IEEE transactions on cybernetics·2026
Same author

Finite-Time Intermittent Anti-Disturbance Control for Discrete-Time Switched Systems With Stochastic Gain Fluctuations: Partial Information Loss Case.

IEEE transactions on cybernetics·2026
Same author

Surgical management of traumatic posterior hip dislocation associated with ipsilateral femoral shaft fracture in a child via the anterior approach: a case report and literature review.

Frontiers in pediatrics·2026

Related Experiment Video

Updated: Mar 19, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Containment Control for Second-Order Multiagent Systems Communicating Over Heterogeneous Networks.

Jiahu Qin, Wei Xing Zheng, Huijun Gao

    IEEE Transactions on Neural Networks and Learning Systems
    |June 23, 2016
    PubMed
    Summary

    This study develops containment control algorithms for second-order multiagent systems with heterogeneous networks. New methods ensure follower agents stay within bounds of multiple leaders, even with complex network interactions.

    More Related Videos

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.9K

    Related Experiment Videos

    Last Updated: Mar 19, 2026

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.2K
    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.9K

    Area of Science:

    • Control Theory
    • Networked Systems
    • Robotics

    Background:

    • Multiagent systems (MAS) are crucial for coordinated behaviors.
    • Containment control in MAS ensures followers remain within leader-defined boundaries.
    • Heterogeneous networks introduce complexities in agent interactions.

    Purpose of the Study:

    • To develop novel containment control algorithms for second-order MAS.
    • To address challenges posed by heterogeneous networks with distinct position and velocity interactions.
    • To analyze containment control under various leader movement scenarios (stationary, constant velocity, time-varying velocity).

    Main Methods:

    • Design of containment control algorithms tailored for different leader motion patterns.
    • Extension of existing algorithms from homogeneous to heterogeneous networks.
    • Development of novel algorithms for directed and undirected interaction topologies.
    • Derivation of sufficient conditions for guaranteed containment behavior.

    Main Results:

    • Algorithms proposed for stationary, constant-speed, and time-varying speed leaders.
    • Novel algorithm developed for directed position and velocity interactions.
    • Sufficient conditions for containment are established and verified.
    • Simulation examples demonstrate the effectiveness of the proposed methods.

    Conclusions:

    • The developed algorithms effectively achieve containment control in second-order MAS over heterogeneous networks.
    • The theoretical findings are validated through comprehensive simulations.
    • The study provides verifiable conditions for ensuring coordinated agent behavior.