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OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results

Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods....

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Autores principales: Abdelkader, Mohamed, Mabrok, Mohamed, Koubaa, Anis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736629/
https://www.ncbi.nlm.nih.gov/pubmed/36501943
http://dx.doi.org/10.3390/s22239240
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author Abdelkader, Mohamed
Mabrok, Mohamed
Koubaa, Anis
author_facet Abdelkader, Mohamed
Mabrok, Mohamed
Koubaa, Anis
author_sort Abdelkader, Mohamed
collection PubMed
description Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications.
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spelling pubmed-97366292022-12-11 OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results Abdelkader, Mohamed Mabrok, Mohamed Koubaa, Anis Sensors (Basel) Article Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications. MDPI 2022-11-28 /pmc/articles/PMC9736629/ /pubmed/36501943 http://dx.doi.org/10.3390/s22239240 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abdelkader, Mohamed
Mabrok, Mohamed
Koubaa, Anis
OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results
title OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results
title_full OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results
title_fullStr OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results
title_full_unstemmed OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results
title_short OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results
title_sort octune: optimal control tuning using real-time data with algorithm and experimental results
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736629/
https://www.ncbi.nlm.nih.gov/pubmed/36501943
http://dx.doi.org/10.3390/s22239240
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