Robot Trajectory Tracking with Self-Tuning Predicted Control

Abstract

A controller that combines self-tuning prediction and control is proposed as a new approach to robot trajectory traking. The controller has two feedback loops: One is used to minimize the prediction error and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated on-line to account for the model uncertainty and the time-varying property of the system. We have described the principle of STPC, analyzed the system performance. and discussed the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.