An estimation based iterative learning control approach for cross-coupled vehicle test systems

Abstract

In this work an estimation based iterative learning control (ILC) approach for a vehicle suspension rig is presented. Slow convergence rates and non-monotonic learning transients of the typically cross-coupled multi-axial system require a high number of system measurements. This significantly damages the specimen before the actual endurance test. In combination with inverse model ILC the presented method uses estimates of the test system outputs in a second iteration domain in addition to real system measurements. The method is tested in an experiment and compared to the conventional inverse model ILC approach. The results show a significant reduction of necessary system measurements without non-monotonic learning transients compared to the conventional ILC method.