  The sit-to-stand movement is a key feature for wide adoption of powered lower
limb orthoses for patients with complete paraplegia. In this paper we study the
control of the ascending phase of the sit-to-stand movement for a minimally
actuated powered lower limb orthosis at the hips. First, we generate a pool of
finite horizon Linear Quadratic Regulator feedback gains, designed under the
assumption that we can control not only the torque at the hips but also the
loads at the shoulders that in reality are applied by the user. Next we conduct
reachability analysis to define a performance metric measuring the robustness
of each controller against parameter uncertainty, and choose the best
controller from the pool with respect to this metric. Then, we replace the
presumed shoulder control with an Iterative Learning Control algorithm as a
substitute for human experiments. Indeed this algorithm obtains torque and
forces at the shoulders that result in successful simulations of the
sit-to-stand movement, regardless of parameter uncertainty and factors
deliberately introduced to hinder learning. Thus it is reasonable to expect
that the superior cognitive skills of real users will enable them to cooperate
with the hip torque controller through training.
