Neuro-generalized minimum variance controller applied to earthquake engineering problems

This paper presents a neural network-based control method applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the effectiveness of the neural network controller and its capability to compensate for structural parameter variations.