Iterative Learning Control With Enhanced Convergence for Discrete-Time Linear Systems

In this paper, a convergence-enhanced iterative learning control algorithm with a tuning parameter is presented for a class of repetitive discrete-time linear single-input single-output systems to achieve a faster convergence of the tracking error. Two convergence conditions related to the tuning parameter are established by the spectral radius of a matrix and the roots of a quadratic equation, respectively. The admissible interval of the tuning parameter that guarantees the convergence of the tracking error is obtained. In addition, the explicit expression of the optimal parameter is derived to guarantee the fastest convergence rate of the tracking error. Finally, the superiority of the proposed algorithm is illustrated by two numerical examples.