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

SFG Algebra

SFG Algebra

In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...

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...

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...

Control System Problem

Control System Problem

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...

Control Systems

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...

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,...

You might also read

Related Articles

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

Sort by

Same author

Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability.

Heliyon·2024

Same author

Systematic solution for multi-model control approaches in nonlinear dynamic systems based on the s-gap metric.

ISA transactions·2024

Same author

Designing a fixed-time observer-based adaptive non-singular sliding mode controller for flexible spacecraft.

ISA transactions·2024

Same author

Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach.

Heliyon·2024

Same author

Neural network identification in nonlinear model predictive control for frequent and infrequent operating points using nonlinearity measure.

ISA transactions·2019

Same author

A novel method for trajectory planning of cooperative mobile manipulators.

Journal of medical signals and sensors·2012

Search research articles

Related Experiment Video

Updated: Sep 29, 2025

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

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

Published on: March 10, 2011

Robustness analysis of Adaptive Model Predictive Control for uncertain non-linear dynamic systems using s-gap metric

Saman Saki¹, Hossein Bolandi¹

¹Department of Electrical Engineering at Iran University of Science and Technology, Iran.

ISA Transactions

|March 27, 2022

Summary

This study introduces a novel method for robust adaptive control systems, enhancing online model identification and stability. It addresses challenges in non-linear systems by integrating generalized stability margin (GSM) into adaptive model predictive control (AMPC).

Keywords:

Gain bounds Graph topology Non-linear gap metric Robust on-line identification and robust model predictive control

More Related Videos

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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

Related Experiment Videos

Last Updated: Sep 29, 2025

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

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

Published on: March 10, 2011

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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

Area of Science:

Control Engineering
Systems Theory
Robust Control

Background:

Robustness analysis of adaptive control systems, particularly for non-linear dynamic systems with un-modelled dynamics, remains a significant challenge.
Existing methods struggle to adequately address unstructured uncertainty and the interplay between controller operation and on-line model identification.

Purpose of the Study:

To develop a systematic solution for robust adaptive control by addressing limitations in current robust control theory.
To present a novel on-line identification method with convergence guarantees and establish a relationship between generalized stability margin (GSM) and identifier convergence.

Main Methods:

Introduced new concepts in robust control theory, including the s-gap metric and generalized stability margin (GSM).
Developed an on-line identification method with convergence proof based on the s-gap metric.
Integrated GSM into the Adaptive Model Predictive Control (AMPC) cost function using linear matrix inequality (LMI) representation for robustness.

Main Results:

Demonstrated a method for on-line identification with convergence guarantees in the sense of the s-gap metric.
Established a relationship between GSM and the identifier convergence area, ensuring stability of AMPC within a defined operating domain.
Identified a trade-off between attraction area size, convergence area size, and closed-loop system robustness.

Conclusions:

The proposed method offers a systematic solution for relating controller robustness and adaptivity in AMPC.
The integration of GSM constraints guarantees stability and defines the attraction area of the closed-loop system.
Simulations and experimental results validate the correctness and effectiveness of the proposed robust adaptive control approach.