Repetitive Process Based Design of PD-Type Iterative Learning Control Laws

Abstract

New results on the design of iterative learning control laws are developed. The analysis is in the repetitive process setting. The iterative learning law combines a PD-type learning function and state feedback, where only a relatively small number of parameters need to be tuned. The analysis is extended to allow different finite frequency range performance specifications, where this facility is relevant to many applications. The design computations required are linear matrix inequality based. The new design is illustrated by a simulation based study on robotic manipulator behavior, where the model used has been constructed from experimental data.