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Optimality in Neuromuscular Systems


Conference Paper


Abstract? We provide an overview of optimal control meth- ods to nonlinear neuromuscular systems and discuss their lim- itations. Moreover we extend current optimal control methods to their application to neuromuscular models with realistically numerous musculotendons; as most prior work is limited to torque-driven systems. Recent work on computational motor control has explored the used of control theory and esti- mation as a conceptual tool to understand the underlying computational principles of neuromuscular systems. After all, successful biological systems regularly meet conditions for stability, robustness and performance for multiple classes of complex tasks. Among a variety of proposed control theory frameworks to explain this, stochastic optimal control has become a dominant framework to the point of being a standard computational technique to reproduce kinematic trajectories of reaching movements (see [12]) In particular, we demonstrate the application of optimal control to a neuromuscular model of the index finger with all seven musculotendons producing a tapping task. Our simu- lations include 1) a muscle model that includes force- length and force-velocity characteristics; 2) an anatomically plausible biomechanical model of the index finger that includes a tendi- nous network for the extensor mechanism and 3) a contact model that is based on a nonlinear spring-damper attached at the end effector of the index finger. We demonstrate that it is feasible to apply optimal control to systems with realistically large state vectors and conclude that, while optimal control is an adequate formalism to create computational models of neuro- musculoskeletal systems, there remain important challenges and limitations that need to be considered and overcome such as contact transitions, curse of dimensionality, and constraints on states and controls.

Author(s): Theodorou, E. A. and Valero-Cuevas, F.
Book Title: 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2010

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)

Cross Ref: p10367
Note: clmc

Links: PDF


  title = {Optimality in Neuromuscular Systems},
  author = {Theodorou, E. A. and Valero-Cuevas, F.},
  booktitle = {32nd Annual International Conference of the  IEEE Engineering in Medicine and Biology Society},
  year = {2010},
  note = {clmc},
  crossref = {p10367}