Semi-Active Control of Structures Using a Neuro-Inverse Model of MR Dampers

Abstract

Abstract. A semi-active controller-based neural network for a 3 story nonlinear benchmark structure equipped with a Magneto Rheological (MR) damper is presented and evaluated. An inverse neural network model (NIMR) is constructed to replicate the inverse dynamics of the MR damper. A Linear Quadratic Gaussian (LQG) controller is also designed to produce the optimal control force. The LQG controller and the NIMR models are linked to control the structure. The e ectiveness of the NIMR is illustrated and veri ed using the simulated response of a full-scale, nonlinear, seismically excited, 3-story benchmark building excited by several historical earthquake records. The semi-active system using the NIMR model is compared to the performance of an active LQG and a Clipped Optimal Control (COC) system, which is based on the same nominal controller as used in the NIMR damper control algorithm. Two passive control systems are also considered and compared. The results demonstrate that by using the NIMR model, the MR damper force can be commanded to follow closely the desirable optimal control force. The results also show that the control system is e ective, and achieves better performance than active LQG and COC system. The optimal passive controller performs better than the NIMR. However, the performance of NIMR will be improved if a more e ective active controller is replaced by a LQG controller.