Active control strategy of structures based on lattice type probabilistic neural network

Abstract

A new neuro-control scheme for active control of structures having a basic structure similar to the Probabilistic Neural Network (PNN) is proposed. It utilizes the lattice pattern of state vector as the training data of PNN, and thus it is called the Lattice Probabilistic Neural Network (LPNN). Comparing the two schemes, PNN takes much time to obtain a control force in the application because it uses all the training patterns. This may delay the control action inevitably. However, in LPNN, the control force is calculated by using only the adjacent information of LPNN input, making the response of LPNN greatly faster than that of PNN. To investigate the general control capability of the proposed algorithm, one-story and three-story buildings under California, El Centro, and Northridge earthquakes are used as test models. Control results of the LPNN are compared with those of the conventional PNN, and these show that the structural responses have been suppressed effectively by the proposed algorithm.