Conditional learning control for alignment initial condition
This article tries to handle the alignment initial condition for contraction mapping based iterative learning control, such that the system can operate continuously without any initial condition reset. This goal is achieved for a class of nonlinear systems through the proposed conditional learning control, which has several advantages over the alternative method, adaptive learning control. The conditional learning control guarantees that sufficient knowledge can be learned to update the input and achieve perfect output tracking, despite the nonidentical initial conditions. The sufficient conditions of either monotonic or strictly monotonic convergence of the input sequence, and the choice of learning gains are given. The performance of the proposed method is illustrated by simulated examples.
iterative learning control nonlinear system convergence steady state