Adaptive control of base-isolated structures against near-field earthquakes using variable friction dampers

This paper investigates the effectiveness of two adaptive control strategies for modulating the control force of variable friction dampers (VFDs) that are employed as semi-active devices in combination with laminated rubber bearings for the seismic protection of buildings. The first controller developed in this study is an adaptive fuzzy neural controller (AFNC). It consists of a direct fuzzy controller with self-tuning scaling factors based on neural networks. A simple neural network is implemented to adjust the input and output scaling factors such that the fuzzy controller effectively determines the command voltage of the damper according to current level of ground motion. A multi-objective genetic algorithm is used to learn the shape of the activation functions of the network. The second controller is based on the simple adaptive control (SAC) method, which is a type of direct adaptive control approach. The objective of the SAC method is to make the plant, the controlled system, track the behavior of the structure with the optimum performance. Here, SAC methodology is employed to obtain the required control force which results in the optimum performance of the structure. For comparison purposes, an optimal linear quadratic Gaussian (LQG) controller is also developed and considered in the simulations together with maximum passive operation of the friction damper. The results reveal that the developed adaptive controllers can successfully improve the seismic response of base-isolated buildings against various types of earthquake.