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

State Space Representation01:27

State Space Representation

755
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
755
Feedback control systems01:26

Feedback control systems

821
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...
821
Open and closed-loop control systems01:17

Open and closed-loop control systems

2.1K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
2.1K
Controller Configurations01:22

Controller Configurations

461
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
461
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

509
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...
509
Load-frequency control01:28

Load-frequency control

828
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
828

You might also read

Related Articles

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

Sort by
Same author

Laparoscopic Versus Open Radical Nephrectomy for Renal Cell Carcinoma: A Retrospective Study.

Indian journal of surgical oncology·2026
Same author

Direct oral anticoagulants versus vitamin K antagonists for atrial fibrillation patients on dialysis: A systematic review and meta-analysis of randomized controlled trials and real-world evidence.

Journal of thrombosis and thrombolysis·2026
Same author

Prevalence, Determinants, and Consequences of Intimate Partner Violence Among Pregnant Women in Egypt: A Systematic Review and Meta-Analysis.

Trauma, violence & abuse·2026
Same author

Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization.

Scientific reports·2026
Same author

MTAS-MENA: adapting the Stroke Access Barrier Index (SABI) to enhance mechanical thrombectomy access in the Middle East and North Africa region.

Frontiers in neurology·2026
Same author

Comprehensive neurointervention training and service capacity in the Middle East & North Africa (NITA-MENA) study.

Neurological research·2026

Related Experiment Video

Updated: Apr 21, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

3.1K

Echo-state network based adaptive fuzzy sliding-mode consensus control scheme for nonlinear multi-agent systems with

Sameh Abd-Elhaleem1, Mostafa Sallam2, Tarek A Mahmoud2

  • 1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt; Department of Computer, Arab East Colleges, Riyadh, Kingdom of Saudi Arabia.

ISA Transactions
|April 19, 2026
PubMed
Summary

This study introduces a hybrid control strategy for nonlinear multi-agent systems (MASs) to improve robustness against disturbances. The novel approach combines adaptive fuzzy sliding-mode control (AFSM), an echo state network (ESN), and a nonlinear disturbance observer unit (NDOU) for enhanced performance.

Keywords:
Adaptive fuzzyChaotic-whale optimization algorithm (CWOA)Echo-state network (ESN)Nonlinear multi-agent system (MAS)Sliding-mode control (SMC)

Related Experiment Videos

Last Updated: Apr 21, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

3.1K

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Nonlinear multi-agent systems (MASs) face challenges from external disturbances and uncertainties.
  • Conventional sliding-mode control (SMC) offers robustness but suffers from chattering.
  • Existing methods often struggle with accurate state estimation and disturbance rejection in complex MASs.

Purpose of the Study:

  • To develop a novel hybrid control strategy for nonlinear MASs.
  • To address limitations of conventional controllers, including chattering and disturbance effects.
  • To enhance robustness, tracking performance, and control efficiency in MASs.

Main Methods:

  • Integration of an adaptive fuzzy sliding-mode (AFSM) controller to mitigate chattering.
  • Utilization of an echo state network (ESN) optimized by the chaotic whale optimization algorithm (CWOA) for improved state estimation.
  • Incorporation of a nonlinear disturbance observer unit (NDOU) for estimating and compensating disturbances and unmeasured states.
  • Lyapunov stability analysis to ensure system stability.

Main Results:

  • The proposed hybrid controller effectively suppresses chattering and enhances disturbance rejection.
  • The optimized ESN improves state estimation accuracy and dynamic adaptability.
  • Synergistic integration leads to fast, robust, and reliable consensus among agents.
  • Simulation results demonstrate superior tracking performance and control efficiency compared to conventional methods.

Conclusions:

  • The novel hybrid control strategy offers a significant advancement for nonlinear MASs.
  • The integrated approach overcomes limitations of single-technique controllers, providing practical effectiveness.
  • This framework enhances the reliability and performance of MASs in uncertain and disturbed environments.