Improvement of trajectory tracking for industrial robot arms by learning control with B-spline

Abstract

This paper describes that a learning control algorithm with B-spline is effective to improve the trajectory tracking accuracy of an industrial robot and shows the results of simulation and experiment. The learning control method consists of two processes: Global Learning (GL) and Local Learning (LL). GL estimates the dynamics of a robot control system and obtains a learning gain matrix used in LL. LL decreases the tracking errors by iterative trial movements and acquires satisfactory tracking accuracy. The learning algorithm needs only measuring of position errors from a desired trajectory and does not require any derivatives of them. As the input trajectories after the convergence of learning are expressed by B-spline curves, they are easily memorized as input patterns corresponding to specified works.