Iterative Learning Control with Advanced Output Data Using Partially Known Impulse Response

摘要

This letter investigates an ADILC (Iterative Learning Control with Advanced Output Data) scheme for nonminimum phase systems using a partially known impulse response. ADILC has a simple learning structure that can be applied to both minimum phase and nonminimum phase systems. However, in the latter case, the overall control time horizon must be considered in the input update law, which makes the dimension of the matrices in the convergence condition very large. Also, this makes it difficult to find a proper learning gain matrix. In this letter, a new sufficient condition is derived from the convergence condition, which can be used to find the learning gain matrix for nonminimum phase systems if we know the first part of the impulse response up to a sufficient order. Based on this, an iterative learning control scheme is proposed using the estimation of the first part of the impulse response for nonminimum phase systems.