A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis
Abstract--- Abstract--- A hybrid neuroprosthesis that uses an electric motor-based wearable exoskeleton and functional electrical stimulation (FES) has a promising potential to restore walking in persons with paraplegia. A hybrid actuation structure introduces effector redundancy, making its automat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-12-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fbioe.2015.00203/full |
| _version_ | 1818602060080218112 |
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| author | Naji A Alibeji Nicholas Andrew Kirsch Nitin eSharma |
| author_facet | Naji A Alibeji Nicholas Andrew Kirsch Nitin eSharma |
| author_sort | Naji A Alibeji |
| collection | DOAJ |
| description | Abstract--- Abstract--- A hybrid neuroprosthesis that uses an electric motor-based wearable exoskeleton and functional electrical stimulation (FES) has a promising potential to restore walking in persons with paraplegia. A hybrid actuation structure introduces effector redundancy, making its automatic control a challenging task because multiple muscles and additional electric motor need to be coordinated. Inspired by the muscle synergy principle, we designed a low dimensional controller to control multiple effectors: FES of multiple muscles and electric motors. The resulting control system may be less complex and easier to control. To obtain the muscle synergy-inspired low dimensional control, a subject-specific gait model was optimized to compute optimal control signals for the multiple effectors. The optimal control signals were then dimensionally reduced by using principal component analysis to extract synergies. Then, an adaptive feedforward controller with an update law for the synergy activation was designed. In addition, feedback control was used to provide stability and robustness to the control design. The adaptive-feedforward and feedback control structure makes the low dimensional controller more robust to disturbances and variations in the model parameters and may help to compensate for other time-varying phenomena (e.g., muscle fatigue). This is proven by using a Lyapunov stability analysis, which yielded semi-global uniformly ultimately bounded tracking. Computer simulations were performed to test the new controller on a 4 degree of freedom gait model. |
| first_indexed | 2024-12-16T13:01:16Z |
| format | Article |
| id | doaj.art-6344d0374d0b4334ae604ef99235fe36 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-16T13:01:16Z |
| publishDate | 2015-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-6344d0374d0b4334ae604ef99235fe362022-12-21T22:30:50ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852015-12-01310.3389/fbioe.2015.00203152938A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking NeuroprosthesisNaji A Alibeji0Nicholas Andrew Kirsch1Nitin eSharma2University of PittsburghUniversity of PittsburghUniversity of PittsburghAbstract--- Abstract--- A hybrid neuroprosthesis that uses an electric motor-based wearable exoskeleton and functional electrical stimulation (FES) has a promising potential to restore walking in persons with paraplegia. A hybrid actuation structure introduces effector redundancy, making its automatic control a challenging task because multiple muscles and additional electric motor need to be coordinated. Inspired by the muscle synergy principle, we designed a low dimensional controller to control multiple effectors: FES of multiple muscles and electric motors. The resulting control system may be less complex and easier to control. To obtain the muscle synergy-inspired low dimensional control, a subject-specific gait model was optimized to compute optimal control signals for the multiple effectors. The optimal control signals were then dimensionally reduced by using principal component analysis to extract synergies. Then, an adaptive feedforward controller with an update law for the synergy activation was designed. In addition, feedback control was used to provide stability and robustness to the control design. The adaptive-feedforward and feedback control structure makes the low dimensional controller more robust to disturbances and variations in the model parameters and may help to compensate for other time-varying phenomena (e.g., muscle fatigue). This is proven by using a Lyapunov stability analysis, which yielded semi-global uniformly ultimately bounded tracking. Computer simulations were performed to test the new controller on a 4 degree of freedom gait model.http://journal.frontiersin.org/Journal/10.3389/fbioe.2015.00203/fullFunctional Electrical Stimulation (FES)Adaptive controlNonlinear ControlHybrid neuroprosthesisTime-Invariant Synergies |
| spellingShingle | Naji A Alibeji Nicholas Andrew Kirsch Nitin eSharma A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis Frontiers in Bioengineering and Biotechnology Functional Electrical Stimulation (FES) Adaptive control Nonlinear Control Hybrid neuroprosthesis Time-Invariant Synergies |
| title | A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis |
| title_full | A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis |
| title_fullStr | A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis |
| title_full_unstemmed | A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis |
| title_short | A Muscle Synergy-inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis |
| title_sort | muscle synergy inspired adaptive control scheme for a hybrid walking neuroprosthesis |
| topic | Functional Electrical Stimulation (FES) Adaptive control Nonlinear Control Hybrid neuroprosthesis Time-Invariant Synergies |
| url | http://journal.frontiersin.org/Journal/10.3389/fbioe.2015.00203/full |
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