Broad learning robust semi-active structural control: A nonparametric approach.

• A novel nonparametric reliability-based output feedback control algorithm is proposed for semi-active structural control. • An adaptive broad learning network is developed to formulate the control strategy using the clipped-optimal control technique. • It is developed adaptively to adopt additional optimal training input–output data pairs. • It incorporates both predicted failure probability and the uncertainty of the underlying structure. • The proposed algorithm provides optimal and robust control regardless of the uncertainty of the structural parameters, external excitation, and structural damage conditions. We propose a novel algorithm for dynamic response suppression via semi-active control devices, which we refer to as broad learning, robust, semi-active control (BLRSAC). To configure the semi-active controller, a nonparametric reliability-based output feedback control strategy is introduced. In particular, an adaptive broad learning network is developed to formulate the control strategy using the clipped-optimal control technique. The learning network is augmented incrementally to adopt additional training data based on the inherited information of the trained learning network. By utilizing a robust failure probability, the training dataset is obtained adaptively to include the training input–output pairs with optimal structural control performance. The robust failure probability we propose incorporates both predicted failure probability and the uncertainty of the underlying structure. Therefore, the resultant control strategy can handle the inevitable uncertainty of the actual control situation to achieve optimal structural control. To examine the efficacy of the proposed BLRSAC algorithm, illustrative examples of a shear building and a three-dimensional braced frame under various external excitation and structural damaging conditions are presented. [ABSTRACT FROM AUTHOR]