Iterative learning control tracks the slowly varying reference trajectory of linear parabolic distributed parameter systems

Abstract

Up to now, based upon the existing results on iterative learning control (ILC) of distributed parameter systems have not been fully utilized for the variable reference trajectory case. This paper proposes a new ILC algorithm for a parabolic distributed parameter system that assumes that the reference trajectory changes slowly with the number of iterations. The sufficient conditions are established such that the tracking error convergent to a bounded domain. And the detail theoretical analysis of the algorithm convergence is also presented clearly under some given conditions. Numerical examples verify the effectiveness of the proposed method.