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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...

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...

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...

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...

Controls in Experiments

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...

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...

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Related Experiment Video

Updated: Dec 1, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research

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Published on: November 24, 2021

Control of COVID-19 system using a novel nonlinear robust control algorithm.

Musadaq A Hadi¹, Hazem I Ali¹

¹Control and Systems Eng. Dept., Control and Systems Eng. Dept., University of Technology, Iraq.

Biomedical Signal Processing and Control

|November 11, 2020

Summary

This summary is machine-generated.

A novel mathematical-engineering strategy using robust control algorithms and optimization techniques effectively manages the COVID-19 epidemic. This approach aids in controlling the nonlinear system until a vaccine becomes available.

Keywords:

COVID-19 Coronavirus Most Valuable Player Algorithm Nonlinear system Robust control algorithm Variable Transformation Technique

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Area of Science:

Epidemiology
Control Theory
Mathematical Modeling

Background:

The COVID-19 pandemic necessitates innovative control strategies beyond traditional suppression and mitigation.
Existing methods require complementary approaches to manage the epidemic's dynamics effectively.

Purpose of the Study:

To introduce a new mathematical-engineering strategy for controlling the COVID-19 epidemic.
To develop a robust control algorithm to compensate for the nonlinear dynamics of the COVID-19 system.

Main Methods:

Application of control theory to manage epidemic instability.
Utilization of Variable Transformation Technique (VTT) for system simplification.
Optimization of controller parameters using the Most Valuable Player Algorithm (MVPA).

Main Results:

The proposed robust control algorithm effectively compensates for the nonlinear COVID-19 system.
Simulation results based on data from Hubei, China, and Lazio, Italy, demonstrate the algorithm's efficacy.
The strategy proved capable of managing epidemic spread in real-world scenarios.

Conclusions:

The developed mathematical-engineering strategy offers an effective method for COVID-19 control.
This approach can be integrated with existing strategies to enhance epidemic management.
The robust control algorithm provides a valuable tool for public health interventions during pandemics.