Assessment of hybrid machine learning models for non-linear system identification of fatigue test rigs

Since a control error always remains in practical, highly dynamic applications, it is necessary to adapt the drive signal, the input of the controller, in such a way that it leads to the desired system response after control has taken place. Due to the complexity of service loads, this process can not be conducted manually. In practice, a dynamic response simulation [1] is conducted, which involves system identification by means of a linear model of the dynamic system. Frequency response function (FRF) models have been used for test rig descriptions since 1976 [2]. Including further developments [3], they are state of the art for system identification and dynamic response simulation.