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To control structures against wind and earthquake excitations, Adaptive Neuro Fuzzy Inference Systems and Neural Networks are combined in this study. The control scheme consists of an ANFIS inverse model of the structure to assess the control force. Considering existing ANFIS controllers, which require a second controller to generate training data, the authors’ approach does not need another controller. To generate control force, active and semi-active devices could be used. Since the active ANFIS inverse controller may not guarantee a satisfactory response due to different uncertainties associated with operating conditions and noisy training data, this paper uses MR dampers as semi-active devices to provide control forces. To overcome the difficulty of tuning command voltage of MR dampers, a neural network inverse model is developed. The effectiveness of the proposed strategy is verified and illustrated using simulated response of the 3-story full-scale nonlinear benchmark building excited by several earthquake records through computer simulation. Moreover, the recommended control algorithm is validated using the wind-excited 76-story benchmark building equipped with MR and TMD dampers. Comparing results with other controllers demonstrates that the proposed method can reduce displacement, drift and acceleration, significantly.

Controlling structures by inverse adaptive neuro fuzzy inference system and MR dampers