  This paper proposes a data-driven method for powered prosthesis control that
achieves stable walking without the need for additional sensors on the human.
The key idea is to extract the nominal gait and the human interaction
information from motion capture data, and reconstruct the walking behavior with
a dynamic model of the human-prosthesis system. The walking behavior of a human
wearing a powered prosthesis is obtained through motion capture, which yields
the limb and joint trajectories. Then a nominal trajectory is obtained by
solving a gait optimization problem designed to reconstruct the walking
behavior observed by motion capture. Moreover, the interaction force profiles
between the human and the prosthesis are recovered by simulating the model
following the recorded gaits, which are then used to construct a force tube
that covers all the interaction force profiles. Finally, a robust Control
Lyapunov Function (CLF) Quadratic Programming (QP) controller is designed to
guarantee the convergence to the nominal trajectory under all possible
interaction forces within the tube. Simulation results show this controller's
improved tracking performance with a perturbed force profile compared to other
control methods with less model information.
