Active vibration control of structural systems with a preview of a future seismic waveform generated by remote waveform observation data and an artificial intelligence-based waveform estimation system

We propose a new active vibration control strategy of structural systems based on the information of future seismic waveform observed in remote observation sites. The observed waveform information of the remote site is transmitted by a waveform transmission network to the structure under control. The waveform transmission network is realized by interconnecting multiple controlled structures and observation sites. By using the remote waveform containing the future information of the disturbance at the location of the controlled structure, we propose an active control method that achieves fairly higher control performance over conventional methodologies. A preview control consisting of the state-feedback and feedforward control (preview action) is adopted as the control law. For the preview action, a future seismic waveform in some time interval is needed. Because the future seismic waveform is not available, the preview action contributing the performance improvement is generally impossible. To get over this difficulty, an artificial intelligence-based waveform estimation system to estimate the future seismic waveform is proposed. The core of the wave estimation system is a multi-layered artificial neural network. Through a small-scale simulation study with a recorded seismic event in Japan, we show that the proposed control method achieves much higher control performance over the optimized H 2 state-feedback control law.