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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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...
45
Feedback control systems01:26

Feedback control systems

297
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...
297
Control Systems01:10

Control Systems

1.1K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.1K
Open and closed-loop control systems01:17

Open and closed-loop control systems

698
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...
698
Load-frequency control01:28

Load-frequency control

140
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...
140
Controller Configurations01:22

Controller Configurations

90
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...
90

You might also read

Related Articles

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

Sort by
Same author

Retraction Note: A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults.

Scientific reports·2026
Same author

RETRACTED: Srivastava et al. Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier. <i>Sensors</i> 2022, <i>22</i>, 3620.

Sensors (Basel, Switzerland)·2026
Same author

Leader follower second order voltage control with disturbance observer for DC microgrids.

Scientific reports·2026
Same author

Load frequency control of a PV-DSTS integrated thermal-hydro power system using a CCSA-optimized fuzzy fractional-order parallel controller.

Scientific reports·2026
Same author

CNN-based compensation of faulty planar phased-array radiation patterns.

Scientific reports·2026
Same author

Retraction Note: Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon.

Scientific reports·2026

Related Experiment Video

Updated: Jun 17, 2025

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

1.6K

Lyapunov-based neural network model predictive control using metaheuristic optimization approach.

Chafea Stiti1, Mohamed Benrabah2, Abdelhadi Aouaichia1

  • 1Laboratory of Electrical Systems and Remote Control, Blida1 University Blida, Ouled Yaïch, Algeria.

Scientific Reports
|August 13, 2024
PubMed
Summary
This summary is machine-generated.

A new Lyapunov-based neural network model predictive control method enhances control of nonlinear systems. This approach ensures stability and demonstrates superior accuracy and speed in motor control applications.

Keywords:
ConstraintsDTBOLyapunov functionMetaheuristicModel predictive controlNeural networkNonlinear systemSquirrel cage induction motor

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Related Experiment Videos

Last Updated: Jun 17, 2025

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

1.6K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Controlling constrained nonlinear systems presents significant challenges in achieving stability and performance.
  • Existing model predictive control (MPC) methods often struggle with computational complexity and convergence speed for complex systems.

Purpose of the Study:

  • To introduce a novel Lyapunov-based neural network model predictive control (NN-MPC) strategy.
  • To leverage a metaheuristic optimization algorithm for efficient problem-solving within the MPC framework.
  • To validate the controller's effectiveness in managing constrained nonlinear systems with fast dynamics.

Main Methods:

  • Utilizing a feedforward neural network as the prediction model within the MPC framework.
  • Employing the driving training based optimization (DTBO) algorithm to solve the constrained optimization problem.
  • Incorporating a Lyapunov function as a constraint in the cost function to guarantee closed-loop stability.

Main Results:

  • The proposed Lyapunov-based NN-MPC with DTBO demonstrated superior accuracy, speed, and robustness in controlling the angular speed of a three-phase squirrel cage induction motor.
  • Performance metrics including mean absolute error, root mean square error, and enhancement percentage were significantly improved compared to other advanced control techniques.
  • The controller exhibited efficient computational performance, indicated by reduced computing time.

Conclusions:

  • The developed Lyapunov-based NN-MPC using DTBO is a highly effective and efficient technique for controlling constrained nonlinear systems.
  • The integration of neural networks and metaheuristic optimization offers a promising direction for advanced control system design.
  • The controller's robustness and performance make it suitable for applications requiring precise and rapid control of dynamic systems.