Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modeling with Differential Equations

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

Open and closed-loop control systems

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 Control

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...

PD Controller: Design

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,...

Feedback control systems

Feedback control systems

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...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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...

You might also read

Related Articles

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

Sort by

Same author

The four-tank control problem: Comparison of two disturbance rejection control solutions.

ISA transactions·2017

Same author

Digital current regulator for proportional electro-hydraulic valves with unknown disturbance rejection.

ISA transactions·2013

Same author

Embedded Model Control: outline of the theory.

ISA transactions·2007

Same author

Embedded model control: application to web winding.

ISA transactions·2007

Same author

Active vibration suppression in a suspended Fabry-Pérot cavity.

ISA transactions·2006

Same author

Nanobalance: an automated interferometric balance for micro-thrust measurement.

ISA transactions·2004

Search research articles

Related Experiment Video

Updated: May 20, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Embedded Model Control calls for disturbance modeling and rejection.

Enrico Canuto¹, Wilber Acuna-Bravo, Andrés Molano-Jimenez

¹Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. enrico.canuto@polito.it

ISA Transactions

|July 6, 2012

Summary

Robust control design ensures system stability despite uncertainties using Embedded Model Control. This method uses ignorance coefficients and noise estimation to maintain control law integrity and reject disturbances.

Related Experiment Videos

Last Updated: May 20, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Area of Science:

Control Engineering
Systems Theory
Robotics

Background:

Model-based control laws are susceptible to parametric uncertainties, potentially compromising closed-loop stability.
Existing methods often require significant modifications to the control law to handle uncertainty.

Purpose of the Study:

To present a robust control design methodology that guarantees closed-loop stability in the presence of parametric uncertainties.
To demonstrate how Embedded Model Control can maintain the integrity of a model-based control law under uncertainty.

Main Methods:

Introducing ignorance coefficients and restricting feedback control effort to ensure stability.
Complementing controllable dynamics with a suitable disturbance dynamics within the Embedded Model Control framework.
Estimating disturbance states using model error (difference between plant and model output) and designing an uncertainty-aware noise estimator.

Main Results:

The model-based control law remains intact even with uncertainties.
The noise estimator prevents destabilizing command-dependent uncertainty components from the model error.
Separation of frequency components by the noise estimator ensures stability recovery and guarantees rejection of low-frequency disturbances.

Conclusions:

Embedded Model Control provides a robust framework for maintaining stability in uncertain systems.
The proposed noise estimation strategy effectively handles parametric uncertainties, enhancing control system reliability.
The methodology offers a systematic approach to robust control design with guaranteed stability and disturbance rejection.