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Selection/control concurrent optimization of BLDC motors for industrial robots
This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431662/ https://www.ncbi.nlm.nih.gov/pubmed/37585384 http://dx.doi.org/10.1371/journal.pone.0289717 |
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author | Padilla-García, Erick Axel Cervantes-Culebro, Héctor Rodriguez-Angeles, Alejandro Cruz-Villar, Carlos Alberto |
author_facet | Padilla-García, Erick Axel Cervantes-Culebro, Héctor Rodriguez-Angeles, Alejandro Cruz-Villar, Carlos Alberto |
author_sort | Padilla-García, Erick Axel |
collection | PubMed |
description | This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators’ parameters modify the robot’s performance, the selection process considers the actuators’ parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor’s energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators’ voltage and a PID controller for actuators’ torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology. |
format | Online Article Text |
id | pubmed-10431662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104316622023-08-17 Selection/control concurrent optimization of BLDC motors for industrial robots Padilla-García, Erick Axel Cervantes-Culebro, Héctor Rodriguez-Angeles, Alejandro Cruz-Villar, Carlos Alberto PLoS One Research Article This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators’ parameters modify the robot’s performance, the selection process considers the actuators’ parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor’s energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators’ voltage and a PID controller for actuators’ torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology. Public Library of Science 2023-08-16 /pmc/articles/PMC10431662/ /pubmed/37585384 http://dx.doi.org/10.1371/journal.pone.0289717 Text en © 2023 Padilla-García et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Padilla-García, Erick Axel Cervantes-Culebro, Héctor Rodriguez-Angeles, Alejandro Cruz-Villar, Carlos Alberto Selection/control concurrent optimization of BLDC motors for industrial robots |
title | Selection/control concurrent optimization of BLDC motors for industrial robots |
title_full | Selection/control concurrent optimization of BLDC motors for industrial robots |
title_fullStr | Selection/control concurrent optimization of BLDC motors for industrial robots |
title_full_unstemmed | Selection/control concurrent optimization of BLDC motors for industrial robots |
title_short | Selection/control concurrent optimization of BLDC motors for industrial robots |
title_sort | selection/control concurrent optimization of bldc motors for industrial robots |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431662/ https://www.ncbi.nlm.nih.gov/pubmed/37585384 http://dx.doi.org/10.1371/journal.pone.0289717 |
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