  Current prosthesis control methods are primarily model-independent - lacking
formal guarantees of stability, relying largely on heuristic tuning parameters
for good performance, and neglecting use of the natural dynamics of the system.
Model-dependence for prosthesis controllers is difficult to achieve due to the
unknown human dynamics. We build upon previous work which synthesized provably
stable prosthesis walking through the use of rapidly exponentially stabilizing
control Lyapunov functions (RES-CLFs). This paper utilizes RES-CLFs together
with force estimation to construct model-based optimization-based controllers
for the prosthesis. These are experimentally realized on hardware with onboard
sensing and computation. This hardware demonstration has formal guarantees of
stability, utilizes the natural dynamics of the system, and achieves superior
tracking to other prosthesis trajectory tracking control methods.
