LEGO NXT for experimental validation of iterative learning control schemes

Iterative Learning Controller (ILC) built on top of conventional controls helps the plant to effectively track its reference signals under repetitive conditions. Research devoted to this has been an extensive topic in last decade and so were the practical applications, that cover manufacturing systems, chemical processes, robotics, etc. In this paper we deal with learning experience of ILC algorithms. It covers how solving real problems in lab provide the students with a holistic approach to understanding control engineering. For this purpose, we have chosen a tracking robot as our plant. We performed a set of experiments on the real platform, a LEGO based robot, which allowed us to show the significant improvement for tracking performance when ILC complements a non-ILC controller in an entertaining and less expensive way.