Neurosemiactive control of 3-storey moment resistant frame with magnetorheological dampers

Abstract

The paper presents a procedure for development of an optimal semi-active neurocontroller for capturing the phenomenological model of a Magnetorheological (MR) damper using Linear Quadratic Regulator (LQR) algorithm for controlling a 3-Storey Steel moment resistant frame (SMRF) model. One of the important aspects of the structural control is the time delay associated with the control algorithm used to predict the control force. AI techniques such as Artifi cial Neural Network (ANN) can be used to improve the effi ciency/performance of the control module. Keeping this in view, the possibility of application of feed forward neural network, implementing LQR algorithm for semi-active control of MR damper in SMRF has been explored. An explicit relation between control force and command signals (voltage) has been developed for the given MR damper. The Neurocontroller is trained and tested with six types of earthquake records scaled to Peak Ground Acceleration (PGA) of Design Basis Earthquake (DBE). This methodology can be further extended to train the ANN corresponding to site-specifi c earthquakes based on the location of the building.