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

PD Controller: Design01:26

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
Controller Configurations01:22

Controller Configurations

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 aligns...
PI Controller: Design01:24

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
Control Systems01:10

Control Systems

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

Open and closed-loop control systems

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 and...
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...

You might also read

Related Articles

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

Sort by
Same author

Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.

Frontiers in big data·2025
Same author

Tree-based machine learning performed in-memory with memristive analog CAM.

Nature communications·2021
Same author

Decision trees within a molecular memristor.

Nature·2021
Same author

Analog content-addressable memories with memristors.

Nature communications·2020
Same author

Simultaneous use of magnetic method in localization of impalpable breast cancer and sentinel lymph nodes detection: initial experience.

Nanomedicine (London, England)·2018
Same journal

Optimization of mode discerning control for nonlinear hybrid systems subject to unknown inputs with applications to active fault diagnosis.

ISA transactions·2026
Same journal

Convergence evaluation of optimization-based stochastic iterative learning control.

ISA transactions·2026
Same journal

Adaptive utility-aware event-triggered reinforcement learning for hybrid attack scheduling against remote state estimation.

ISA transactions·2026
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
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

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

Robust model predictive control design with input constraints.

Vojtech Veselý1, Danica Rosinová, Martin Foltin

  • 1Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovak Republic. vojtech.vesely@stuba.sk

ISA Transactions
|October 27, 2009
PubMed
Summary
This summary is machine-generated.

This study presents a robust model predictive control (MPC) for linear systems with input constraints. The novel approach ensures system stability and reduces computational load for faster control applications.

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Area of Science:

  • Control Engineering
  • Systems Theory
  • Applied Mathematics

Background:

  • Model Predictive Control (MPC) is crucial for managing complex systems.
  • Linear polytopic systems with input constraints present significant control design challenges.
  • Existing MPC methods can be computationally intensive, limiting their application to slower systems.

Purpose of the Study:

  • To design a robust output/state model predictive control for linear polytopic systems.
  • To address challenges posed by input constraints in predictive control.
  • To develop a computationally efficient MPC scheme suitable for faster dynamics.

Main Methods:

  • Derivation of a new predictive and control horizon model as a linear polytopic system.
  • Utilization of a Lyapunov function approach to guarantee quadratic stability and cost.
  • Application of invariant sets and a Soft Variable-Structure Control (SVSC)-like algorithm to enforce input constraints.

Main Results:

  • A novel predictive and control horizon model was successfully derived.
  • Quadratic stability and guaranteed cost for the closed-loop system were proven.
  • Input constraints were effectively managed using invariant sets and an SVSC-like algorithm.
  • The proposed scheme demonstrated significantly reduced on-line computation compared to existing MPC literature.

Conclusions:

  • The developed robust MPC scheme effectively handles linear polytopic systems with input constraints.
  • The reduced computational load makes the control design applicable to faster dynamic systems.
  • This work advances the practical implementation of MPC in diverse engineering applications.