Fast and incremental neural associative memory based approach for adaptive open-loop structural control in high-rise buildings

Abstract

A novel neural associative memory-based structural control method, coined as AMOLCO, is proposed in this study. AMOLCO is an open-loop control system that autonomously and incrementally learns to suppress the structural vibration caused by dynamic loads such as wind excitations and earthquakes to stabilize high-rise buildings. First, AMOLCO incrementally learns the associative pair of input excitation from either winds or earthquakes and the corresponding output control response generated by standard optimal control only under a single simple condition (i.e., low wind conditions). After learning for a short period of time, i.e., 15 min, AMOLCO becomes capable of efficiently suppressing more intense structural vibrations such as those caused by very strong winds or even earthquakes. In this study, evaluation of the AMOLCO method is performed by using the physical simulation data. The results show that the control signal generated by AMOLCO is similar to that generated by the state-of-the-art control system used in a building. In addition, the resulting control signal is tested on a realistic simulation to affirm that the signal can control the structures. These results show that for the first time, AMOLCO offers another approach of structural control, which is inexpensive and stable similar to a standard open-loop system and also adaptive against disturbances and dynamic changes similar to a closed-loop system. © 2011 Springer-Verlag.