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An evolutionary intelligent control system for a flexible joints robot

Alejandro Pena, Juan C. Tejada Orcid Logo, Juan David Gonzalez-Ruiz Orcid Logo, Lina María Sepúlveda-Cano Orcid Logo, Francisco Chiclana, Fabio Caraffini Orcid Logo, Mario Gongora

Applied Soft Computing, Volume: 135, Start page: 110043

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1016/j.asoc.2023.110043

Abstract

In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlembeck process with reversion to the mean. We a...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62420
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spelling 2023-02-13T13:00:09.1268433 v2 62420 2023-01-24 An evolutionary intelligent control system for a flexible joints robot d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-01-24 SCS In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlembeck process with reversion to the mean. We also propose a Stochastic Flexible - Adaptive Neural Integrated System (SF-ANFIS) to identify and control the RFJ with two degrees of freedom. For the configuration of the model, we use two adaptive strategies. One strategy is based on the Generalised Delta Rule (GDR). In contrast, a second strategy is based on the EDA-MAGO algorithm (Estimation Distribution Algorithms - Multi-dynamics Algorithm for Global Optimisation), improving online learning. We considered three stages for analysing and validating the proposed SF-ANFIS model: a first identification stage, a second stage defined by the adaptive control process, and a final stage or cancellation of oscillations. Results show that, for the identification stage, the SF-ANFIS model showed better statistical indices than the MADALINE model in control for the second joint, which presents the greatest oscillations; among those that stand out, the IOA (0.9955), VG (1.0012) and UAPC2 (-0.0003). For the control stage, The SF-ANFIS model showed, in a general way, the best behaviour in the system’s control for both joints, thanks to the capacity to identify and cancel oscillations based on the advanced sampling that defines the EDA algorithm. For the cancellation of the oscillations stage, the SF-ANFIS achieved the best behaviour, followed by the MADALINE model, where it is highlighted the UAPC2 (0.9525) value. Journal Article Applied Soft Computing 135 110043 Elsevier BV 1568-4946 Adaptive Neural Fuzzy Integrated Systems (ANFIS); Stochastic model; System control; Robotics; Ornstein–Uhlenbeck (OU) 1 3 2023 2023-03-01 10.1016/j.asoc.2023.110043 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2023-02-13T13:00:09.1268433 2023-01-24T08:44:38.7657224 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alejandro Pena 1 Juan C. Tejada 0000-0003-1195-3379 2 Juan David Gonzalez-Ruiz 0000-0003-4425-7687 3 Lina María Sepúlveda-Cano 0000-0003-1749-816x 4 Francisco Chiclana 5 Fabio Caraffini 0000-0001-9199-7368 6 Mario Gongora 7 Under embargo Under embargo 2023-01-26T13:46:09.7054683 Output 5970346 application/pdf Accepted Manuscript true 2024-01-23T00:00:00.0000000 ©2023 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title An evolutionary intelligent control system for a flexible joints robot
spellingShingle An evolutionary intelligent control system for a flexible joints robot
Fabio Caraffini
title_short An evolutionary intelligent control system for a flexible joints robot
title_full An evolutionary intelligent control system for a flexible joints robot
title_fullStr An evolutionary intelligent control system for a flexible joints robot
title_full_unstemmed An evolutionary intelligent control system for a flexible joints robot
title_sort An evolutionary intelligent control system for a flexible joints robot
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Alejandro Pena
Juan C. Tejada
Juan David Gonzalez-Ruiz
Lina María Sepúlveda-Cano
Francisco Chiclana
Fabio Caraffini
Mario Gongora
format Journal article
container_title Applied Soft Computing
container_volume 135
container_start_page 110043
publishDate 2023
institution Swansea University
issn 1568-4946
doi_str_mv 10.1016/j.asoc.2023.110043
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlembeck process with reversion to the mean. We also propose a Stochastic Flexible - Adaptive Neural Integrated System (SF-ANFIS) to identify and control the RFJ with two degrees of freedom. For the configuration of the model, we use two adaptive strategies. One strategy is based on the Generalised Delta Rule (GDR). In contrast, a second strategy is based on the EDA-MAGO algorithm (Estimation Distribution Algorithms - Multi-dynamics Algorithm for Global Optimisation), improving online learning. We considered three stages for analysing and validating the proposed SF-ANFIS model: a first identification stage, a second stage defined by the adaptive control process, and a final stage or cancellation of oscillations. Results show that, for the identification stage, the SF-ANFIS model showed better statistical indices than the MADALINE model in control for the second joint, which presents the greatest oscillations; among those that stand out, the IOA (0.9955), VG (1.0012) and UAPC2 (-0.0003). For the control stage, The SF-ANFIS model showed, in a general way, the best behaviour in the system’s control for both joints, thanks to the capacity to identify and cancel oscillations based on the advanced sampling that defines the EDA algorithm. For the cancellation of the oscillations stage, the SF-ANFIS achieved the best behaviour, followed by the MADALINE model, where it is highlighted the UAPC2 (0.9525) value.
published_date 2023-03-01T04:22:01Z
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score 11.017797

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