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Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative...

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Autores principales: Azeez, Muhammad I., Abdelhaleem, A. M. M., Elnaggar, S., Moustafa, Kamal A. F., Atia, Khaled R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397350/
https://www.ncbi.nlm.nih.gov/pubmed/37532737
http://dx.doi.org/10.1038/s41598-023-38855-7
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author Azeez, Muhammad I.
Abdelhaleem, A. M. M.
Elnaggar, S.
Moustafa, Kamal A. F.
Atia, Khaled R.
author_facet Azeez, Muhammad I.
Abdelhaleem, A. M. M.
Elnaggar, S.
Moustafa, Kamal A. F.
Atia, Khaled R.
author_sort Azeez, Muhammad I.
collection PubMed
description The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative surface (SMC-PID) is employed for maneuver control. An improved artificial bee colony algorithm with multi-elite guidance (MGABC) is utilized to obtain optimal values for the sliding surface and switching mode gain and attain the best performance for the robot maneuver system. The selection of the MGABC algorithm is based on its efficient exploration and exploitation techniques. The performance of the optimized SMC-PID robotic system is compared against other optimization algorithms found in existing literature, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). The implemented controller effectively reduces the tracking error to 0.00691 radians, eliminates chattering phenomena in the control effort, and demonstrates robustness against disturbances and noise. The controller ensures that the objective function (OBJF) is minimized, with 0.954% increase in OBJF under low disturbance and noise conditions and 14.55% under severe disturbance and noise conditions. Moreover, the optimized controller exhibits resilience to variations in payload mass analysis, with the percentage increase in OBJF values ranging from 5.726% under low uncertainty conditions to 18.887% under severe uncertainty conditions. Flexible-link maneuvers (FLM) offer advantages such as improved safety and increased operating speeds in real-world applications. In this study, we investigated the impact of joint flexibility on the performance of the FLM system. Our proposed controller demonstrated superior tracking performance, characterized by minimal vibrations in the movement of the end effector.
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spelling pubmed-103973502023-08-04 Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output Azeez, Muhammad I. Abdelhaleem, A. M. M. Elnaggar, S. Moustafa, Kamal A. F. Atia, Khaled R. Sci Rep Article The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative surface (SMC-PID) is employed for maneuver control. An improved artificial bee colony algorithm with multi-elite guidance (MGABC) is utilized to obtain optimal values for the sliding surface and switching mode gain and attain the best performance for the robot maneuver system. The selection of the MGABC algorithm is based on its efficient exploration and exploitation techniques. The performance of the optimized SMC-PID robotic system is compared against other optimization algorithms found in existing literature, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). The implemented controller effectively reduces the tracking error to 0.00691 radians, eliminates chattering phenomena in the control effort, and demonstrates robustness against disturbances and noise. The controller ensures that the objective function (OBJF) is minimized, with 0.954% increase in OBJF under low disturbance and noise conditions and 14.55% under severe disturbance and noise conditions. Moreover, the optimized controller exhibits resilience to variations in payload mass analysis, with the percentage increase in OBJF values ranging from 5.726% under low uncertainty conditions to 18.887% under severe uncertainty conditions. Flexible-link maneuvers (FLM) offer advantages such as improved safety and increased operating speeds in real-world applications. In this study, we investigated the impact of joint flexibility on the performance of the FLM system. Our proposed controller demonstrated superior tracking performance, characterized by minimal vibrations in the movement of the end effector. Nature Publishing Group UK 2023-08-02 /pmc/articles/PMC10397350/ /pubmed/37532737 http://dx.doi.org/10.1038/s41598-023-38855-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Azeez, Muhammad I.
Abdelhaleem, A. M. M.
Elnaggar, S.
Moustafa, Kamal A. F.
Atia, Khaled R.
Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output
title Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output
title_full Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output
title_fullStr Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output
title_full_unstemmed Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output
title_short Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output
title_sort optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397350/
https://www.ncbi.nlm.nih.gov/pubmed/37532737
http://dx.doi.org/10.1038/s41598-023-38855-7
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