Iterative Learning Control analysis for automotive testbed applications

Iterative Learning Control (ILC) represents an innovative paradigm in the control of dynamic systems, aimed at progressively improving system performance through iterative learning from repeatable reference tracks. This thesis aims to explore and analyse different ILC approaches to achieve accelerometer reference tracking. The research work first focuses on the dynamic modelling of the Quarter Car model system and the more generic Full Car model. Starting from the differential equations, the State Space model is then derived to facilitate the implementation in the Simulink environment. Then, road profiles will be generated to be used as input to obtain the accelerometer references. These profiles will be generated according to the ISO 8608 classification in order to get data as consistent as possible with the real ones. After introducing the iterative algorithm, the results obtained in simulation using different approaches will be shown and discussed, both in the SISO case using the Quarter Car model and in the MIMO case using the Full Car model. Furthermore, after exploring and evaluating some strategies to increase the robustness of the ILC, the algorithm will be tested in the presence of repetitive and non-repetitive disturbances and compared with more traditional controllers. Finally, some tests of the algorithm in a real environment will be carried out, making a comparison with those obtained previously with simulations. In conclusion, this thesis contributes to a better understanding and implementation of the Iterative Learning Control algorithm by analysing its theoretical foundations and the relative results obtained through simulations and real experiments.