Bio-inspired sensing and actuating architectures for feedback control of civil structures. (27th February 2019) - Peckens, C A; Cook, I; Fogg, C
Abstract: Civil structures, such as buildings and bridges, are constantly at risk of failure due to external environmental loads, such as earthquakes or strong winds. To minimize the effects of these loads, active feedback control systems have been proposed but such systems still face numerous challenges which impede their widespread adoption. In order to overcome many of these challenges, inspiration can be drawn from the signal processing and actuating techniques employed by the biological central nervous system to develop a bio-inspired control algorithm. In this study the front-end, signal processing techniques employed by biological sensory systems, and in particular the mammalian auditory system, are drawn upon in order to alleviate computations at the actuation node. This results in a simplistic control law that is a weighted combination of input information about the structure's response such that F   =   WN, where F is the applied control force, W is a pre-determined weighting matrix, and N is a deconstructed representation of the structural response to the applied excitation. There is no empirical solution for deriving an optimal weighting matrix, W, and in this study numerous methods are explored in order to determine values for this matrix that produce the most effective control. These methods include particle swarm optimization, artificial neural networks, and optimal control theory. The various weighting matrices are integrated into the proposed bio-inspired control algorithm and applied in simulation to a five story benchmark structure. These methods are also compared to a traditional linear quadratic regulator (LQR) to gain insight into the overall performance of the bio-inspired control algorithm. Of the three training techniques, the particle swarm optimization technique offers the most effective control which is comparable in performance to the traditional LQR.