Iterative learning of optimal control - Case study of the gantry robot

Abstract

In [15] the authors proposed an iterative learning algorithm for searching for optimal control of linear dynamic systems. This algorithm has been preliminary tested on the laser power control for the cladding process. The aim of this paper is to present a case study of a similar algorithm when applied to control Z-axis of a gantry robot. The original algorithm from [15] has to be modified in order to cover the case when the tracking signal is the output of the system instead of its whole state, as in [15]. The obtained results indicate a fast rate of convergence of the learning algorithm. One can also observe how learning of the shapes of the optimal input and output signals are convergent.