Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm

Abstract

This research presents designing a control system to reduce seismic responses of structures. Semi-active control of a magnetorheological (MR) damper is used to improve seismic behavior of a 3-story building implementing neural-fuzzy controller made of adaptive neuro-fuzzy inference system (ANFIS) to determine damper input voltage. Both premise and consequent parameters of fuzzy membership and output functions of ANFIS have the ability for training and improvement but most researchers have focused on just consequent parameters. In order to optimize the controller performance, an approach is proposed in this paper where both premise and consequent parameters of fuzzy functions in an ANFIS network are adjusted simultaneously by genetic algorithm (GA). In order to assess the effectiveness of the designed control system, its function is numerically studied on a benchmark 3-story building and is compared to those of a neural network predictive control (NNPC) algorithm, linear quadratic Gaussian (LQG) and clipped optimal control (COC) systems in terms of seismic performance. The results showed desirable performance of the (ANFIS +GA + membership functions + result function) ANFIS–GA–MFR controller in considerably reducing the structure responses under different earthquakes. The proposed controller showed 30 and 39% reductions in peak story drift (J1) and normed story drift (J4) respectively compared to the NNPC controller, 32 and 44% reductions in J1 and J4 respectively compared to the LQG controller, and 27 and 38% reductions in J1 and J4 respectively compared to the COC controller. The proposed controller effectively reduced acceleration and base shear level compared to the uncontrolled state and had a performance relatively similar to those of three other controllers – for instance, it reduced the maximum level acceleration (J2) 10% higher than COC. Also, the results showed that the ANFIS–GA–MFR controller has more efficiency than the basic ANFIS controller, on average about 20%.