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


1 Department of Civil Engineering,Ferdowsi University of Mashhad

2 Department of Civil Engineering,Sharif University of Technology


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.