A Neural Network Controller for Trajectory Control of Industrial Robot Manipulators

Abstract

This paper addresses the issue of trajectory tracking control based on a neural network controller for industrial manipulators. A new control scheme is proposed based on neural network technology and linear feedback approach for tracking a planned trajectory. In detail, the control system is designed with two parallel subsystems designed separately. One is a linear controller, and another one is neural network controller. The former is designed for trajectory tracking error regulation, the later for force/torque generation required by the designed dynamic trajectory. A leaning law for online weight updating of the neural network controller is derived based on simplified dynamic model of the robot. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example for trajectory tracking control experiments. Simulations and experiments are carried out on AdeptOne robot. From the simulation and experimental results, the effectiveness and usefulness of the proposed control system are confirmed.