Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis

The sit-to-stand (STS) movement is a key feature for the wide adoption of powered lower limb orthoses (PLLOs) for patients with complete paraplegia. In this article, we study the control of the ascension phase of the STS movement for a minimally actuated PLLO at the hips. A particularly important objective of the design is to ensure robustness to variations in parameters of the users, such as weight. 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 must be 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. Of course, in practice, the loads at the shoulder are ultimately provided by the user, while the computer control is only applied to the hips. Therefore, in the second part of this article, we remove the assumption of computer control for the shoulder loads and study how the shoulder actions could be learned by a human during rehabilitation and physical therapy. As an abstraction of this process, we choose an Iterative Learning Control algorithm to replace the nominal shoulder control. Indeed, this algorithm obtains torque and forces at the shoulders that result in successful simulations of the STS 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.