Neural network-based bounded control of robotic exoskeletons without velocity measurements

Abstract

This paper proposes a novel neural output feedback trajectory tracking controller for robotic exoskeletons. The controller is developed by defining auxiliary dynamics, and utilizing an adaptive feedforward neural network (NN) term to compensate for unknown nonlinear dynamics of the system. The proposed approach only needs position information in both the controller and adaptation rule of the NN weight matrix. In addition, the controller provides an a priori bounded control command. The performance of the controller is validated through simulations and experiments conducted on a lower-limb robotic exoskeleton. It is shown through experiments that the NN term of the controller has assist-as-needed property, such that its contribution in the controller output decreases when the user can follow the desired trajectory in a rehabilitation task.