Design issues on robustness and convergence of iterative learning controller
In this paper, the following two important issues of iterative learning control are considered : i) the robustness to the initial error and ii) the convergence in the sense of sup-norm. The main theme of this paper is how to design an iterative learning controller in order to make the learning algorithm to be more robust to the initial error and/or guarantee the exponential convergence in the sense of the sup-norm. To this end, we restrict our attention to the PD-type learning law and study its proper-ties about the two issues. The results show that the pure error term in PD-type learning law is closely related to the two issues. Based on the investigated properties of the PD-type learning law, a design guide-line is given for the selection of the learning gains.
iterative learning control initial error lambda-norm