Error Corrected References and Acceleration of Norm Optimal iterative Learning Control

Iterative learning control is a control design method for high performance tracking applications. In this paper a novel mechanism for accelerating the convergence of a well-known Norm Optimal Iterative Learning Control (NOILC) Algorithm is presented by modifying the reference signal each iteration using the previously measured tracking error. The change is equivalent to successive application of a gradient and NOILC iteration and can be interpreted as an augmentation of the `feedback plus feedforward' structure of NOILC by adding further `feedforward' data from the last iteration. The change is interpreted in terms of the spectrum of the error update operator and the annihilation of spectral components of the error signal. Convergence of the proposed algorithm is analysed rigorously and numerical examples are given to demonstrate the effectiveness of the proposed method.