Data-driven modeling for damping and positioning control of gantry crane

Flexible structural vibrations of large gantry cranes are a well-known issue. Several control strategies were proposed for this problem, which require a detailed dynamical model. Since the modeling of large-scaled structures, as well as of significant nonlinear friction effects is complicated and time consuming, a direct data-driven dynamic reconstruction based on the Koopman operator theory is proposed. Such identification results in a higher order linear state-space model, which reflects the nonlinear behavior in the broad dynamical range of the system and does not depend on a specific operating point. In this paper, a data-driven linear model identification is investigated for a simulation model, as well as for an experimental laboratory crane. The damping and positioning control dynamics based on the identified model were studied and it was shown, that the resulting performance is comparable to model-based nonlinear approaches.