Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation
Research in lower limb wearable robotic control has largely focused on reducing the metabolic cost of walking or compensating for a portion of the biological joint torque, for example, by applying support proportional to estimated biological joint torques. However, due to different musculotendon uni...
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Cambridge University Press
2025-01-01
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| Series: | Wearable Technologies |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2631717625000015/type/journal_article |
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| author | Mahdi Nabipour Gregory S. Sawicki Massimo Sartori |
| author_facet | Mahdi Nabipour Gregory S. Sawicki Massimo Sartori |
| author_sort | Mahdi Nabipour |
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| description | Research in lower limb wearable robotic control has largely focused on reducing the metabolic cost of walking or compensating for a portion of the biological joint torque, for example, by applying support proportional to estimated biological joint torques. However, due to different musculotendon unit (MTU) contractile speed properties, less attention has been given to the development of wearable robotic controllers that can steer MTU dynamics directly. Therefore, closed-loop control of MTU dynamics needs to be robust across fiber phenotypes, that is ranging from slow type I to fast type IIx in humans. The ability to perform closed-loop control the in-vivo dynamics of MTUs could lead to a new class of wearable robots that can provide precise support to targeted MTUs for preventing onset of injury or providing precision rehabilitation to selected damaged tissues. In this paper, we introduce a novel closed-loop control framework that utilizes nonlinear model predictive control to keep the peak Achilles tendon force within predetermined boundaries during diverse range of cyclic force production simulations in the human ankle plantarflexors. This control framework employs a computationally efficient model comprising a modified Hill-type MTU contraction dynamics component and a model of the ankle joint with parallel actuation. Results indicate that the closed-form muscle-actuation model’s computational time is in the order of microseconds and is robust to different muscle contraction velocity properties. Furthermore, the controller achieves tendon force control within a time frame below
$ 18\mathrm{ms} $
, aligning with the physiological electromechanical delay of the MTU and facilitating its potential for future real-world applications. |
| format | Article |
| id | doaj-art-b1584c8e58094bc0a9f747501db18874 |
| institution | OA Journals |
| issn | 2631-7176 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
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| series | Wearable Technologies |
| spelling | doaj-art-b1584c8e58094bc0a9f747501db188742025-08-20T02:14:35ZengCambridge University PressWearable Technologies2631-71762025-01-01610.1017/wtc.2025.1Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuationMahdi Nabipour0https://orcid.org/0000-0002-6263-4469Gregory S. Sawicki1Massimo Sartori2Neuromuscular Robotics Laboratory, Department of Biomechanical Engineering, University of Twente, Enschede, the NetherlandsHuman Physiology of Wearable Robotics (PoWeR) laboratory, George W. Woodruff School of Mechanical Engineering, School of Biological Sciences and Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USANeuromuscular Robotics Laboratory, Department of Biomechanical Engineering, University of Twente, Enschede, the NetherlandsResearch in lower limb wearable robotic control has largely focused on reducing the metabolic cost of walking or compensating for a portion of the biological joint torque, for example, by applying support proportional to estimated biological joint torques. However, due to different musculotendon unit (MTU) contractile speed properties, less attention has been given to the development of wearable robotic controllers that can steer MTU dynamics directly. Therefore, closed-loop control of MTU dynamics needs to be robust across fiber phenotypes, that is ranging from slow type I to fast type IIx in humans. The ability to perform closed-loop control the in-vivo dynamics of MTUs could lead to a new class of wearable robots that can provide precise support to targeted MTUs for preventing onset of injury or providing precision rehabilitation to selected damaged tissues. In this paper, we introduce a novel closed-loop control framework that utilizes nonlinear model predictive control to keep the peak Achilles tendon force within predetermined boundaries during diverse range of cyclic force production simulations in the human ankle plantarflexors. This control framework employs a computationally efficient model comprising a modified Hill-type MTU contraction dynamics component and a model of the ankle joint with parallel actuation. Results indicate that the closed-form muscle-actuation model’s computational time is in the order of microseconds and is robust to different muscle contraction velocity properties. Furthermore, the controller achieves tendon force control within a time frame below $ 18\mathrm{ms} $ , aligning with the physiological electromechanical delay of the MTU and facilitating its potential for future real-world applications.https://www.cambridge.org/core/product/identifier/S2631717625000015/type/journal_articlelocomotionwalkinghoppingmuscle fiber phenotypeankle plantarflexorsachilles tendonmusculotendon unithill-type musclepredictive force controlinjury preventionwearable robotics |
| spellingShingle | Mahdi Nabipour Gregory S. Sawicki Massimo Sartori Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation Wearable Technologies locomotion walking hopping muscle fiber phenotype ankle plantarflexors achilles tendon musculotendon unit hill-type muscle predictive force control injury prevention wearable robotics |
| title | Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation |
| title_full | Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation |
| title_fullStr | Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation |
| title_full_unstemmed | Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation |
| title_short | Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation |
| title_sort | predictive control of musculotendon loads across fast and slow twitch muscles in a simulated system with parallel actuation |
| topic | locomotion walking hopping muscle fiber phenotype ankle plantarflexors achilles tendon musculotendon unit hill-type muscle predictive force control injury prevention wearable robotics |
| url | https://www.cambridge.org/core/product/identifier/S2631717625000015/type/journal_article |
| work_keys_str_mv | AT mahdinabipour predictivecontrolofmusculotendonloadsacrossfastandslowtwitchmusclesinasimulatedsystemwithparallelactuation AT gregoryssawicki predictivecontrolofmusculotendonloadsacrossfastandslowtwitchmusclesinasimulatedsystemwithparallelactuation AT massimosartori predictivecontrolofmusculotendonloadsacrossfastandslowtwitchmusclesinasimulatedsystemwithparallelactuation |