Berniker - 2009 - Simplified and effective motor control based on muscle synergies ...

Citation

Berniker M, Jarc A, Bizzi E, Tresch MC. Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics. Proc Natl Acad Sci. 2009 May;106(18):7601-6. PUBMED

10 Word Summary

Nervous control utilizes natural dynamics to simplify control.

Abstract

The basic hypothesis of producing a range of behaviors using a small set of motor commands has been proposed in various forms to explain motor behaviors ranging from basic reflexes to complex voluntary movements. Yet many fundamental questions regarding this long-standing hypothesis remain unanswered. Indeed, given the prominent nonlinearities and high dimensionality inherent in the control of biological limbs, the basic feasibility of a low-dimensional controller and an underlying principle for its creation has remained elusive. We propose a principle for the design of such a controller, that it endeavors to control the natural dynamics of the limb, taking into account the nature of the task being performed. Using this principle, we obtained a low-dimensional model of the hindlimb and a set of muscle synergies to command it. We demonstrate that this set of synergies was capable of producing effective control, establishing the viability of this muscle synergy hypothesis. Finally, by combining the low-dimensional model and the muscle synergies we were able to build a relatively simple controller whose overall performance was close to that of the system's full-dimensional nonlinear controller. Taken together, the results of this study establish that a low-dimensional controller is capable of simplifying control without degrading performance.

Notes

• Nonlinear system "balanced" into a lower order model.
  • I imagine this is similar to the reduced representation (separation of zero dynamics) from non-linear systems.
• The controller was also reduced in dimension by collapsing the null-space.
  • Interestingly the reduced command space appears to match the experimental synergies.
• Since "balancing" depends on the output variable, defining a different output variable would have resulted in a different solution.
  • Their conclusion for this is that this results in a controller that is only beneficial for a particular type of behavior and is not robust for all scenarios.