Neural network velocity field control of robotic exoskeletons with bounded input

Velocity field control (VFC) is an alternative approach for motion control of robotic systems. It has advantages over the trajectory tacking problem when the timing in the desired task is of less importance in the application of interest. Recently, this control strategy has been emerged as a promising control method in robot-aided rehabilitation. Therefore, this paper addresses the problem of VFC for robotic exoskeletons with dynamic uncertainties. Most existing VFC methods require some knowledge of the dynamic model of the robot, which is usually difficult in practice to precisely identify. Consequently, this paper design an adaptive neural network VFC method to compensate for the dynamic uncertainties. The controller gives a priori bounded control command in order to take into account the saturation of actuators. The controller performance is validated through simulation and experimental studies on a two-degree-of-freedom lower-limb robotic exoskeleton.