Ahead of the End Dynamic Time Warping Distance Algorithm Application in Iterative Learning Control

Abstract

In this paper, we present a new iterative learning control (ILC) design for a class of linear systems. Ahead of the end dynamic time warping distance (EADTW) algorithm proposed as a method to solve optimization problems in iterative learning control. In the algorithm, after each trail, using EADTW comparative field method for solving optimization problems, the input data and the error data between model output and real output are used to revise plant model, and the new plant model will be used in next trail. The model modifying device is designed for non-linear plant and also can be used in linear plant. The results show that the proposed accelerated algorithms are capable of ensuring monotonic error norm reductions and the convergence speed of the algorithm is increased. Realization of the algorithms is discussed and numerical simulations are provided for comparative purposes and to demonstrate the numerical performance and effectiveness of the proposed methods.