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 predetermined 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.
Wireless sensor networks (WSNs) have emerged as a reliable, low-cost alternative to the traditional wired sensing paradigm. While such networks have made significant progress in the field of structural monitoring, significantly less development has occurred for feedback control applications. Previous work in WSNs for feedback control has highlighted many of the challenges of using this technology including latency in the wireless communication channel and computational inundation at the individual sensing nodes. This work seeks to overcome some of those challenges by drawing inspiration from the real-time sensing and control techniques employed by the biological central nervous system and in particular the mammalian cochlea. A novel bio-inspired wireless sensor node was developed that employs analog filtering techniques to perform time-frequency decomposition of a sensor signal, thus encompassing the functionality of the cochlea. The node then utilizes asynchronous sampling of the filtered signal to compress the signal prior to communication. This bio-inspired sensing architecture is extended to a feedback control application in order to overcome the traditional challenges currently faced by wireless control. In doing this, however, the network experiences high bandwidths of low-significance information exchange between nodes, resulting in some lost data. This study considers the impact of this lost data on the control capabilities of the bio -inspired control architecture and finds that it does not significantly impact the effectiveness of control.
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