Semi-active control of structures using neuro-predictive algorithm for MR dampers

A semi-active control method for a seismically excited nonlinear benchmark building equipped with a magnetorheological (MR) damper is presented and evaluated. A linear quadratic Gaussian (LQG) controller is designed to estimate the optimal control force. The required voltage for the MR damper to produce the control force estimated by LQG controller is calculated by a neural network predictive control algorithm (NNPC). The LQG controller and the NNPC are linked to control the structure. The coupled LQG and NNPC system are then used to train a semi-active neuro-controller designated as SANG which produces the necessary control voltage that actuates the MR damper. The effectiveness of the NNPC and SANC is illustrated and verified using simulated response of a 3-story full-scale, nonlinear; seismically excited; benchmark building excited by several historical earthquake records. The semi-active system using the NNPC algorithm is compared with the performances of passive as well as active and clipped optimal control (COC) systems, which are based on the same nominal controller as is used in the NNPC algorithm. The results demonstrate that the SANC algorithm is quite effective in seismic response reduction for wide range of motions from moderate to severe seismic events, compared with the passive systems and performs better than active and COC systems. Copyright (C) 2008 John Wiley & Sons, Ltd.