Reducing the effect of initial condition offsets using selective previous cycle data

Repositioning is required in mechanical systems performing repetitive tasks. The effect of poor accuracy of repositioning on tracking performance of iterative learning control has been fully understood. The iterative learning ensures the system output to follow the desired trajectory with a specified error bound, proportional to the bound on initial condition offsets. In this paper, varying-order learning method is proposed to enhance tracking performance by lowering the size of the error bound. A discrete-time initial rectifying action is introduced, by which the system output achieves the desired trajectory jointed smoothly with a transient trajectory from the starting position. Practical schemes are presented based on the varying-order learning and illustrated by numerical results of a single rigid link example.