Data-driven optimal predictive control of seismic induced vibrations in frame structures

Complex civil engineering structural systems are prone to seismic induced vibrations during which their inherent dynamical features are displayed often causing discrepancies with the prediction of classical idealized models. In the paper, effective control of such systems is pursued by a novel data-driven modeling and a control methodology based on a technique from machine learning: Regression Trees. A training process to create a state–space model based on partitioning the dataset is illustrated. State- and output-feedback are derived using the recursively identified model in order to reach a suitable performance event for an unknown structure. A benchmark frame structure has been used to demonstrate the effectiveness of the entire procedure.