Combined gradient and Iterative Learning Control method for magnetostatic inverse problem

In this paper, a new approach to solve the magnetostatic inverse problem is proposed. The goal of the paper is to place magnetic sources and to specify their locations and intensities from the measurements of a desired magnetic field in the air. In this work, it is assumed that the magnetic sources are coils which their locations and ampere turns must be determined. By using gradient method, coils locations are specified by finding extremum of the desired measured magnetic field and with the Iterative Learning Control, coils ampere turns are determined. Selection of correction term in Iterative Learning Control is the most important part of the controller design which dramatically affects the convergence of the method. The most important merit of the proposed method is its simplicity for implementation. The simulation results of the method show the accuracy and effectiveness of the proposed technique.