Iterative learning control schemes for a class of nonlinear systems: Theory and real-time implementation

Iterative Learning Control (ILC) is a powerful intelligent tool for stabilization of repetitive-task systems. However, choosing and fixing the gains of ILC schemes is not an easy task due to the fact that information propagation is present in two independent directions. In this paper, we propose new sufficient linear matrix inequality conditions for the design of iterative learning trackers for a special class of discrete-time nonlinear systems. The control designs have been testified by the application of different real-time experiments using dsPIC 33 micro-controller and software in the loop simulation. The application of the developed results to speed control of a DC motor subject to a nonlinear friction shows satisfactory tracking performance in real time.