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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

63
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
63
Feedback control systems01:26

Feedback control systems

286
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...
286
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

96
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...
96
Open and closed-loop control systems01:17

Open and closed-loop control systems

637
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...
637
PD Controller: Design01:26

PD Controller: Design

185
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,...
185
State Space Representation01:27

State Space Representation

163
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...
163

You might also read

Related Articles

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

Sort by
Same author

Community-Led Transformation in Practice: A Framework for Trust-Driven Community-Academic Partnerships to Advance Public Health.

Journal of immigrant and minority health·2026
Same author

Game, Set, and Match: A Scoping Review of Matching Characteristics for Control and Intervention Groups in Adaptive Behavioral Interventions for Physical Activity or Healthy Eating Designs for Populations with Overweight and Obesity.

Behavioral medicine (Washington, D.C.)·2026
Same author

Bridging the divide in digital therapeutics (DTx): Partnership strategies for broader representation across DTx development and deployment.

PLOS digital health·2026
Same author

An Early-Stage Digital Therapeutic Intervention to Enhance Affective Response During Physical Activity Among Adults With Overweight or Obesity: Benchmark-Driven Formative Testing Study.

JMIR human factors·2026
Same author

A Scoping Review of Sensor-Based Capture of Eating and Drinking Occasions That Could Be Used for Enhancing Personalized Nutrition Interventions in Real Time.

Advances in nutrition (Bethesda, Md.)·2025
Same author

Gut microbiota variation drives differential performance in leaf beetles across host plants.

Microbiome·2025
Same journal

Dynamic Modeling and System Identification of User Engagement in mHealth Interventions using a Bayesian Approach for Missing Data Imputation.

Control engineering practice·2025
Same journal

Adaptive Personalized Prior-Knowledge-Informed Model Predictive Control for Type 1 Diabetes.

Control engineering practice·2022
Same journal

Prior Informed Regularization of Recursively Updated Latent-Variables-Based Models with Missing Observations.

Control engineering practice·2021
Same journal

Control-oriented physiological modeling of hemodynamic responses to blood volume perturbation.

Control engineering practice·2018
Same journal

Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.

Control engineering practice·2017
Same journal

Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.

Control engineering practice·2014
See all related articles

Related Experiment Video

Updated: Jun 5, 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

3DoF-KF HMPC: A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems.

Owais Khan1, Mohamed El Mistiri1, Sarasij Banerjee1

  • 1Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287 USA.

Control Engineering Practice
|December 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid model predictive control (HMPC) for hybrid systems, enhancing setpoint tracking and disturbance rejection. The advanced control scheme ensures robustness and performance in complex, uncertain environments.

Keywords:
Model predictive control of hybrid systemsepidemic controlhealthcare decision supportmHealthprocess controlproduction planning and controlrobust control

More Related Videos

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

2.4K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K

Related Experiment Videos

Last Updated: Jun 5, 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
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

2.4K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K

Area of Science:

  • Control Systems Engineering
  • Hybrid Systems Analysis
  • Optimization Theory

Background:

  • Hybrid systems present significant control challenges due to their mixed continuous and discrete dynamics.
  • Existing control strategies often struggle with robustness against disturbances and uncertainties inherent in hybrid systems.

Purpose of the Study:

  • To develop and validate a novel hybrid model predictive control (HMPC) scheme for hybrid systems within a mixed logical dynamical (MLD) framework.
  • To enhance setpoint tracking precision and disturbance rejection capabilities of controllers for hybrid systems.
  • To demonstrate the versatility and effectiveness of the proposed HMPC across diverse applications.

Main Methods:

  • Formulation and design of a hybrid model predictive control (HMPC) scheme.
  • Integration of a three degrees-of-freedom (3DoF) tuning method for precise control.
  • Inclusion of setpoint and disturbance anticipation for proactive performance enhancement.
  • Utilization of slack variables to maintain feasibility of the mixed-integer quadratic programming problem.

Main Results:

  • The HMPC scheme effectively manages hybrid dynamics, achieving precise setpoint tracking.
  • Demonstrated robustness against measured and unmeasured disturbances and system uncertainty.
  • Successful application in diverse case studies including production-inventory systems, physical activity interventions, and epidemic prevention.

Conclusions:

  • The proposed HMPC algorithm provides a robust and effective solution for controlling hybrid systems with complex dynamics.
  • The controller exhibits strong performance in setpoint tracking and disturbance rejection, even under nonlinearity and uncertainty.
  • The HMPC's adaptability is confirmed through successful implementation in varied, demanding real-world scenarios.