Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system
A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors...
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| Veröffentlicht in: | Medical & biological engineering & computing 2009-05, Vol.47 (5), p.533-542 |
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| creator | Blana, Dimitra Kirsch, Robert F Chadwick, Edward K |
| description | A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4° in ideal conditions, and less than 10° even in the case of considerable fatigue and external disturbances. |
| doi_str_mv | 10.1007/s11517-009-0479-3 |
| format | Article |
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The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4° in ideal conditions, and less than 10° even in the case of considerable fatigue and external disturbances.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-009-0479-3</identifier><identifier>PMID: 19343388</identifier><language>eng</language><publisher>Berlin/Heidelberg: Berlin/Heidelberg : Springer-Verlag</publisher><subject>Algorithms ; Arm - physiology ; Biomechanics ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Computer Applications ; Control algorithms ; Controllers ; Design ; Elbow ; Electric Stimulation Therapy - methods ; Feedback - physiology ; Human Physiology ; Humans ; Imaging ; Models, Anatomic ; Movement ; Muscle fatigue ; Muscle, Skeletal - physiology ; Musculoskeletal system ; Neural networks ; Neural Networks, Computer ; Paralysis ; Performance evaluation ; Radiology ; Special Issue - Review ; Spinal cord ; Spinal Cord Injuries - rehabilitation ; Studies ; Upper Extremity - physiology</subject><ispartof>Medical & biological engineering & computing, 2009-05, Vol.47 (5), p.533-542</ispartof><rights>International Federation for Medical and Biological Engineering 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c619t-a889309bfbec617e774d3846836b01a4b8ef66e1f6612b760e9b1444ca83e51e3</citedby><cites>FETCH-LOGICAL-c619t-a889309bfbec617e774d3846836b01a4b8ef66e1f6612b760e9b1444ca83e51e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-009-0479-3$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-009-0479-3$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,778,782,883,27907,27908,41471,42540,51302</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19343388$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Blana, Dimitra</creatorcontrib><creatorcontrib>Kirsch, Robert F</creatorcontrib><creatorcontrib>Chadwick, Edward K</creatorcontrib><title>Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4° in ideal conditions, and less than 10° even in the case of considerable fatigue and external disturbances.</description><subject>Algorithms</subject><subject>Arm - physiology</subject><subject>Biomechanics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Computer Applications</subject><subject>Control algorithms</subject><subject>Controllers</subject><subject>Design</subject><subject>Elbow</subject><subject>Electric Stimulation Therapy - methods</subject><subject>Feedback - physiology</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Imaging</subject><subject>Models, Anatomic</subject><subject>Movement</subject><subject>Muscle fatigue</subject><subject>Muscle, Skeletal - physiology</subject><subject>Musculoskeletal system</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Paralysis</subject><subject>Performance evaluation</subject><subject>Radiology</subject><subject>Special Issue - Review</subject><subject>Spinal cord</subject><subject>Spinal Cord Injuries - rehabilitation</subject><subject>Studies</subject><subject>Upper Extremity - 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The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. 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| ispartof | Medical & biological engineering & computing, 2009-05, Vol.47 (5), p.533-542 |
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| subjects | Algorithms Arm - physiology Biomechanics Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Computer Applications Control algorithms Controllers Design Elbow Electric Stimulation Therapy - methods Feedback - physiology Human Physiology Humans Imaging Models, Anatomic Movement Muscle fatigue Muscle, Skeletal - physiology Musculoskeletal system Neural networks Neural Networks, Computer Paralysis Performance evaluation Radiology Special Issue - Review Spinal cord Spinal Cord Injuries - rehabilitation Studies Upper Extremity - physiology |
| title | Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system |
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