Locomotion Envelopes for Adaptive Control of Powered Ankle Prostheses

In this paper we combine Gaussian process regression and impedance control, to illicit robust, anthropomorphic, adaptive control of a powered ankle prosthesis. We learn the non-linear manifolds which guide how locomotion variables temporally evolve, and regress that surface over a velocity range to create a manifold. The joint set of manifolds, as well as the temporal evolution of the gait-cycle duration is what we term a locomotion envelope. Current powered prostheses have problems adapting across speeds. It is likely that humans rely upon a control strategy which is adaptable, can become more robust and accurate with more data and provides a nonparametric approach which allows the strategy to grow with the number of observations. We demonstrate such a strategy in this study and successfully simulate locomotion well beyond our training data. The method we propose is based on common physical features observed in numerous human subjects walking at different speeds. Based on the derived locomotion envelopes we show that ankle power increases monotonically with speed among all subjects. We demonstrate our methods in simulation and human experiments, on a powered ankle foot prosthesis to demonstrate the effectiveness of the method.