Earthquake Response Reduction of Building Structures Using Learning-Based Lattice Pattern Active Controller

Abstract

An active vibration control technique for building structures using a learning-based lattice pattern controller (LBLPC) is proposed in this article. The training pattern of the LBLPC is composed of a lattice form for the control force and a state vector. The training pattern was trained by a learning rule using the gradient descent method (GDM) in earthquakes. The LBLPC calculates the control force using only the adjacent input information, thus making the corresponding calculation process much faster. A three-story building in the El Centro earthquake was used to train the LBLPC. And the California and Northridge earthquakes were used to verify the performance of the proposed method. In order to prove the control capability of the LBLPC, the control results of the LBLPC were compared with those of a lattice type probabilistic neural network (LPNN) in a numerical example. The results demonstrated that the proposed LBLPC algorithm reduces the response of the building structure during earthquakes more effectively than the LPNN.