Closed-Loop Deep Neural Network-Based FES Control for Human Limb Tracking

Functional electrical stimulation (FES) can be used as rehabilitative treatment for lost motor neuron function in people with neurological disorders. This paper considers a leg extension machine coupled to a participant for FES-induced closed-loop lower-limb tracking of a desired trajectory. FES-induced control faces challenges as the muscle dynamics exhibit nonlinear behaviors and have unstructured uncertainty. A closed-loop data-driven deep neural network (DNN)-based adaptive control method for FES-induced lower-limb position trajectory tracking is developed. A Lyapunov-based stability analysis is used to develop a closed-loop state-feedback adaptation law for the outer-layer weights of the DNN, which is combined with a feedback controller to yield semi-global asymptotic tracking.