Robot Learning Control Based on Recurrent Neural Network Inverse Model

A non-linear model based feedforward and feedback controller for a robot arm is described. This controller can control a nonlinear plant such as a robot whose dynamics is initially unknown. In the feedforward part, a recurrent neural network (RNN) is used to model the inverse dynamics of the robot. In the feedback part, a PD controller is added to handle unmodeled dynamics and disturbances. Furthermore, an add-on learning controller is established to reduce tracking errors for repetitive tasks. The controller is validated with the control of a simulated two-joint manipulator. Simulation results show that the controller can successfully learn the inverse dynamics of a robot, perform an accurate tracking for a general trajectory, and improve its own performance over the repetitions of a trajectory.