The application of optimal control theory in the study of musculoskeletal motion was investigated. The research includes a comparative study of muscle models of varying complexity. Four such models ranging in complexity from simple input-output models to very sophisticated phenomenological models were examined. A model was developed which was able to strike a compromise between model complexity and practicability in musculoskeletal motion applications.
In the solution process several optimal optimal control algorithms were compared to determine their ability to handle the large scale nonlinear constrained optimization problems typically encountered in these studies. These algorithms were used to solve four different problems of varying complexity.
The selected muscle model and optimal control algorithm were used to solve the muscle-force sharing problem of gait analysis. The objective function used is the metabolic energy consumption. This quantity was described empirically by deriving appropriate expressions for its components in terms of the state and control variables of the muscle model. The results using this dynamic optimization algorithm are compared with similar results obtained using a static optimization approach.