Discrete direct adaptive ELM controller for active vibration control of nonlinear base isolation buildings
Hybridization of base isolation and active control is a promising alternative to suppress the seismic vibrations in civil structures. The base-isolation system introduces hysteretic/frictional nonlinearity into the structure and hence there is a need to develop a nonlinear adaptive control approach to subdue the vibrations. In this paper, the authors propose discrete direct adaptive ELM controller for active control of nonlinear base isolated building, subjected to a set of near-fault earthquakes. A single hidden layer neural controller with random selection of input weights compensates the nonlinearity and provides desired vibration suppression. The neural controller is based on the extreme learning machine algorithm with random input weight selection and output weights are updated using Lyapunov like adaptation rule. The proposed discrete adaptive control law provides necessary stability and vibration suppression in nonlinear base isolated building. Simulation studies have been carried out using the full-scale three dimensional benchmark eight-storey building comprising hysteretic lead-rubber base-isolation. One earthquake record and perturbed model is used to train the controller offline. The offline trained controller is adapted online using the actual full-scale model for a wide range of near-fault earthquakes. The results clearly show that the proposed controller suppresses the vibration significantly without increasing superstructure responses. The comprehensive performance measures are compared with existing results reported in the literature.
active control extreme learning machine base-isolated building direct adaptive control lyapunov stability