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

207
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...
207
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.2K
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...
1.2K
Feedback control systems01:26

Feedback control systems

521
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...
521
Multimachine Stability01:25

Multimachine Stability

261
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:
261
Control System Problem01:21

Control System Problem

218
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
218
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

134
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...
134

You might also read

Related Articles

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

Sort by
Same author

Nature-Inspired Multi-Level Thresholding Integrated with CNN for Accurate COVID-19 and Lung Disease Classification in Chest X-Ray Images.

Diagnostics (Basel, Switzerland)·2025
Same author

Refining COVID-19 Lesion Segmentation in Lung CT Scans Using Swarm Intelligence and Evolutionary Algorithms.

The international journal of medical robotics + computer assisted surgery : MRCAS·2025
Same author

Automated brain tumour segmentation from multi-modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method.

The international journal of medical robotics + computer assisted surgery : MRCAS·2022
Same author

A sophisticated and provably grayscale image watermarking system using DWT-SVD domain.

The Visual computer·2022
Same author

An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net).

Sensors (Basel, Switzerland)·2022
Same author

Model Predictive Control with Integrated Model Reduction for a Continuous Lactide Ring-Opening Polymerization Process.

ACS omega·2022
Same journal

Hybrid vehicle state estimation using closed-form liquid neural networks and nonlinear Kalman filtering.

ISA transactions·2026
Same journal

Cross-coupled synchronization control strategy for rebar binding robots based on impedance control.

ISA transactions·2026
Same journal

Gas flow tracking for electronic pressure control system in gas chromatography under state constraints and hysteresis：An innovative fuzzy adaptive control approach.

ISA transactions·2026
Same journal

Stackelberg differential game-based fuzzy adaptive hierarchical optimal control for a nonlinear system with unknown dynamics.

ISA transactions·2026
Same journal

Composite fault-tolerant predictive control strategy for PMSM demagnetization faults.

ISA transactions·2026
Same journal

Bias-compensated Q-learning for optimal tracking control under denial-of-service attacks.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Oct 23, 2025

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

2.7K

Multi-objective generalized predictive control for switching systems under unknown switching sequences.

Ali Thamallah1, Anis Sakly1, Faouzi M'Sahli1

  • 1Research Laboratory "Automatic, Electrical Systems and Environment" LAS2E, National Engineering School of Monastir (ENIM), University of Monastir, Ibn El Jazzar, 5019 Skanes, Tunisia.

ISA Transactions
|August 21, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new generalized predictive control (GPC) for unstable switching systems. The novel GPC method effectively stabilizes and tracks systems with unknown switching signals, outperforming existing methods.

Keywords:
Generalized predictive controlMulti objective optimization problemSwitching systemUnknown switching sequence

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

11.9K
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

Related Experiment Videos

Last Updated: Oct 23, 2025

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

2.7K
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

11.9K
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

Area of Science:

  • Control Engineering
  • Systems Science

Background:

  • Switching systems often exhibit complex dynamics, including unstable modes, non-minimum phase characteristics, and variable dead-times.
  • Undetermined switching signals in these systems pose significant challenges for traditional control strategies.

Purpose of the Study:

  • To develop a robust generalized predictive control (GPC) strategy for discrete-time switching systems.
  • To address challenges posed by unstable modes, unknown switching signals, non-minimum phase behavior, and variable dead-times.

Main Methods:

  • A novel predictive control law is derived by solving a dynamic multi-objective optimization problem.
  • The control formulation integrates subsystem behaviors and switching phases for adaptive control.
  • The method is inspired by standard GPC, retaining its desirable features.

Main Results:

  • The developed GPC strategy demonstrates enhanced stability and tracking performance for switching systems.
  • Simulation results on four benchmark examples confirm the method's effectiveness under unknown switching sequences.
  • Comparative analysis shows superior performance over the Multi-Criteria Predictive Control (MOMPC) method.

Conclusions:

  • The proposed GPC method offers an effective solution for controlling and stabilizing challenging discrete-time switching systems.
  • The controller adapts to system dynamics across different modes, ensuring reliable performance.
  • This approach provides a significant advancement in predictive control for complex systems.