Monotonic Convergent Iterative Learning Controller Design Based on Interval Model Conversion

This paper presents the design of a robust iterative learning controller for the case of a plant with interval model uncertainty in the "A-matrix" of its state space description. First order perturbation theory is utilized to find bounds on the eigenvalues and eigenvectors of the powers of A when A is an interval matrix. These bounds are then used for calculation of the interval uncertainty of the Markov matrix, which can then be used to design an iterative learning controller that ensures monotonic convergence for all systems in the interval plant