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Multi-Body Dynamic Simulation - Computed-Muscle Control

Computation of muscle excitation patterns that produce coordinated movements of muscle-actuated dynamic models is an important and challenging problem. Using dynamic optimization to compute excitation patterns comes at a large computational cost, which has limited the use of muscle-actuated simulations. We have developed a new algorithm, which we call computed muscle control, that uses static optimization along with feedforward and feedback controls to drive the kinematic trajectory of a musculoskeletal model toward a set of desired kinematics.
Schematic of the computed muscle control algorithm. The algorithm is applied at each integration time step of a forward dynamic simulation. A set of desired accelerations (q-double-dot) is computed that will drive the generalized coordinates and speeds of the model (q and q-dot ) toward the experimental kinematics (qexp and q-dot). The positive constants kv and kp are feedback gains for the velocity errors eq-dot and position errors eq.
We illustrate the algorithm by computing a set of muscle excitations that drive a 30-muscle, 3-degree-of-freedom model of pedaling to track measured pedaling kinematics and forces. Only ten minutes of computer time were required to compute muscle excitations that reproduced the measured pedaling dynamics, which is over two orders of magnitude faster than conventional dynamic optimization techniques. The speed and accuracy of this new algorithm improves the feasibility of using detailed musculoskeletal models to simulate and analyze movement.
Associated Publications

Thelen, Anderson, and Delp. "Generating dynamic simulations of movement using computed muscle control." Journal of Biomechanics, 2003. (Download PDF)

Thelen and Anderson. "Using computed muscle control to generate forward dynamic simulations of human walking from experimental data." Journal of Biomechanics, 2005 (Download PDF)