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Model Reference Predictive Adaptive Control for Large-Scale Soft Robots

Past work has shown model predictive control (MPC) to be an effective strategy for controlling continuum joint soft robots using basic lumped-parameter models. However, the inaccuracies of these models often mean that an integral control scheme must be combined with MPC. In this paper we present a n...

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Detalles Bibliográficos
Autores principales: Hyatt, Phillip, Johnson, Curtis C., Killpack, Marc D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806097/
https://www.ncbi.nlm.nih.gov/pubmed/33501321
http://dx.doi.org/10.3389/frobt.2020.558027
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author Hyatt, Phillip
Johnson, Curtis C.
Killpack, Marc D.
author_facet Hyatt, Phillip
Johnson, Curtis C.
Killpack, Marc D.
author_sort Hyatt, Phillip
collection PubMed
description Past work has shown model predictive control (MPC) to be an effective strategy for controlling continuum joint soft robots using basic lumped-parameter models. However, the inaccuracies of these models often mean that an integral control scheme must be combined with MPC. In this paper we present a novel dynamic model formulation for continuum joint soft robots that is more accurate than previous models yet remains tractable for fast MPC. This model is based on a piecewise constant curvature (PCC) assumption and a relatively new kinematic representation that allows for computationally efficient state prediction. However, due to the difficulty in determining model parameters (e.g., inertias, damping, and spring effects) as well as effects common in continuum joint soft robots (hysteresis, complex pressure dynamics, etc.), we submit that regardless of the model selected, most model-based controllers of continuum joint soft robots would benefit from online model adaptation. Therefore, in this paper we also present a form of adaptive model predictive control based on model reference adaptive control (MRAC). We show that like MRAC, model reference predictive adaptive control (MRPAC) is able to compensate for “parameter mismatch" such as unknown inertia values. Our experiments also show that like MPC, MRPAC is robust to “structure mismatch” such as unmodeled disturbance forces not represented in the form of the adaptive regressor model. Experiments in simulation and hardware show that MRPAC outperforms individual MPC and MRAC.
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spelling pubmed-78060972021-01-25 Model Reference Predictive Adaptive Control for Large-Scale Soft Robots Hyatt, Phillip Johnson, Curtis C. Killpack, Marc D. Front Robot AI Robotics and AI Past work has shown model predictive control (MPC) to be an effective strategy for controlling continuum joint soft robots using basic lumped-parameter models. However, the inaccuracies of these models often mean that an integral control scheme must be combined with MPC. In this paper we present a novel dynamic model formulation for continuum joint soft robots that is more accurate than previous models yet remains tractable for fast MPC. This model is based on a piecewise constant curvature (PCC) assumption and a relatively new kinematic representation that allows for computationally efficient state prediction. However, due to the difficulty in determining model parameters (e.g., inertias, damping, and spring effects) as well as effects common in continuum joint soft robots (hysteresis, complex pressure dynamics, etc.), we submit that regardless of the model selected, most model-based controllers of continuum joint soft robots would benefit from online model adaptation. Therefore, in this paper we also present a form of adaptive model predictive control based on model reference adaptive control (MRAC). We show that like MRAC, model reference predictive adaptive control (MRPAC) is able to compensate for “parameter mismatch" such as unknown inertia values. Our experiments also show that like MPC, MRPAC is robust to “structure mismatch” such as unmodeled disturbance forces not represented in the form of the adaptive regressor model. Experiments in simulation and hardware show that MRPAC outperforms individual MPC and MRAC. Frontiers Media S.A. 2020-10-05 /pmc/articles/PMC7806097/ /pubmed/33501321 http://dx.doi.org/10.3389/frobt.2020.558027 Text en Copyright © 2020 Hyatt, Johnson and Killpack. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Hyatt, Phillip
Johnson, Curtis C.
Killpack, Marc D.
Model Reference Predictive Adaptive Control for Large-Scale Soft Robots
title Model Reference Predictive Adaptive Control for Large-Scale Soft Robots
title_full Model Reference Predictive Adaptive Control for Large-Scale Soft Robots
title_fullStr Model Reference Predictive Adaptive Control for Large-Scale Soft Robots
title_full_unstemmed Model Reference Predictive Adaptive Control for Large-Scale Soft Robots
title_short Model Reference Predictive Adaptive Control for Large-Scale Soft Robots
title_sort model reference predictive adaptive control for large-scale soft robots
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806097/
https://www.ncbi.nlm.nih.gov/pubmed/33501321
http://dx.doi.org/10.3389/frobt.2020.558027
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