A discrete-time iterative learning control law with exponential rate of convergence

When dealing with the convergence properties of iterative learning controllers, an exponential rate of convergence is desirable. That means a suitable norm of the error trajectory should be reduced from cycle to cycle. In this paper a discrete-time iterative learning controller for single input single output systems is presented. It works with a reduced sampling rate in order to guarantee an exponential rate of convergence. The controller is robust with respect to model uncertainties and excites the system well for performing a system identification. A simulation example shows that the ILC with reduced sampling rate can even cope with initial state error