LAUSR

Model predictive control (MPC) is an optimal control algorithm in which the current control action is obtained by solving an optimization problem in the presence of hard and soft constraints in the finite time horizons sequentially. In most cases, neglecting the effects of the external loads in predicting the future responses of the structures lead to inaccurate control action. Therefore, it could be beneficial to consider the effects of external loads in the future within the MPC to improve its accuracy. In this paper, a developed model predictive control (DMPC) scheme is introduced. For this purpose, a forecasting seismic excitation model is formulated by two sequential autoregressive (AR) models. One of those estimates the future output of the seismic excitation and the second one enhances the estimation accuracy. Then, the efficiency of the presented approach is demonstrated by the numerical study of two benchmark buildings equipped with an active tuned mass damper (ATMD). The performance of the proposed MPC is finally compared with the conventional and ideal MPCs. The numerical outputs prove the competency and higher conformity of the proposed MPC with the ideal one almost in all of the cases. Twelve benchmark performance indices are also utilized for determining the superiority of the method. The average conformity values for all of the performance indices for the proposed method in the three- and nine-story buildings are by up to 17.75% and 9% more than the values in conventional one, respectively.