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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

217
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...
217
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

632
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
632
Multimachine Stability01:25

Multimachine Stability

263
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:
263
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

147
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...
147
Feedback control systems01:26

Feedback control systems

532
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...
532

You might also read

Related Articles

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

Sort by
Same author

Intramolecular Silylarylation of α-Olefins Enabled by Disulfide Co-Catalysis and Maintained by Proton-into-Silylium Interconversion.

Angewandte Chemie (International ed. in English)·2026
Same author

A general controller-based dynamic linearized model-free adaptive control and its application to PMLSM.

ISA transactions·2026
Same author

Direct Design and Analysis of Distributed Iterative Learning Control.

IEEE transactions on cybernetics·2025
Same author

Data-Driven Point-to-Point Finite-Iteration Learning Control for a Class of Nonlinear Systems With Output Saturation.

IEEE transactions on cybernetics·2025
Same author

Model-Free Adaptive Fault-Tolerant Formation Control for Nonlinear MIMO Multiagent Systems Over Fading Channels.

IEEE transactions on cybernetics·2025
Same author

Predictive iterative learning control based on averaging technology for networked systems with fading channels and data loss.

ISA transactions·2025
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Nov 1, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.1K

Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault.

Shuangshuang Xiong, Zhongsheng Hou

    IEEE Transactions on Neural Networks and Learning Systems
    |June 25, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel adaptive control algorithm for multi-agent systems (MAS) facing sensor faults. The method ensures stable formation control despite unknown system dynamics and sensor errors.

    More Related Videos

    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

    878
    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

    1.9K

    Related Experiment Videos

    Last Updated: Nov 1, 2025

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
    08:18

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

    Published on: August 15, 2020

    5.1K
    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

    878
    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

    1.9K

    Area of Science:

    • Robotics and Control Systems
    • Artificial Intelligence
    • Nonlinear Systems Theory

    Background:

    • Multi-agent systems (MAS) require robust control for coordinated tasks.
    • Unknown system dynamics and sensor faults pose significant challenges in MAS control.
    • Existing methods often struggle with heterogeneous, non-affine, nonlinear systems.

    Purpose of the Study:

    • To develop a data-driven, distributed formation control algorithm for unknown, heterogeneous, non-affine, nonlinear discrete-time MIMO MAS with sensor faults.
    • To address challenges posed by unknown system dynamics and sensor failures.
    • To ensure bounded formation errors in the presence of uncertainties.

    Main Methods:

    • Dynamic linearization technique from model-free adaptive control (MFAC) to create a virtual data model.
    • Radial basis function neural network (RBFNN) for fault-free system training and fault estimation.
    • Design of estimation laws for unknown fault and system parameters using only measured input-output data.
    • Contraction mapping and mathematical induction for boundedness analysis of formation error.

    Main Results:

    • A distributed model-free adaptive controller structure was successfully constructed.
    • Estimation laws for unknown fault and system parameters were designed using I/O data.
    • The boundedness of the formation error was mathematically proven.
    • Simulation examples demonstrated the effectiveness of the proposed algorithm.

    Conclusions:

    • The proposed data-driven distributed formation control algorithm effectively handles unknown heterogeneous non-affine nonlinear discrete-time MIMO MAS with sensor faults.
    • The integration of MFAC, RBFNN, and advanced mathematical analysis provides a robust solution for complex MAS control problems.
    • The algorithm ensures stable formation and bounded errors, validated through simulations.