Iterative learning control of a long range tribometer under repetitive perturbation and friction

The present paper proposes an iterative learning control (ILC) algorithm for accurate force tracking in a long range tribometer. The device is a nonlinear single input single output plant affected by repetitive and non repetitive perturbations in the output. The former perturbations result from the repetitive nature of the tests, and the latter ones are caused by friction phenomena in the guiding elements. Due to the characteristics of the device, it has been necessary to develop specific solutions to assure a robust behavior: on the one hand, analysis and identification techniques for the perturbations, and on the other hand, a spatial synchronization. The finally developed controller combines ILC with feedforward and feedback commands. The system is experimentally validated and the results show a substantial improvement in the performance compared to more classical approaches.