Experimental Validation of Bio-Inspired Control: 4 Story Shear Structure

Abstract

With the rise of rapid urbanization, skyscrapers are increasingly common, making them vulnerable to environmental pressures such as high winds and earthquakes. These forces threaten the structural stability of tall buildings, necessitating the development of active control methods to mitigate their effects and maintain structural integrity. Traditional control systems utilize numerous sensing nodes that feed data into a centralized system, which then determines the appropriate actions for the actuators. However, this centralized approach can introduce substantial lag due to the overwhelming amount of data being processed. By transitioning to a decentralized wireless system, where sensors directly feed data to control nodes, scalability is improved, and lag is reduced. However, the wireless nature increases the risk of data loss. The proposed solution draws inspiration from the biological central nervous system, with sensing nodes performing upfront signal processing. A weighting matrix determines how data is transmitted to motor neurons, which decide actuator firing and force application. Using the Linear Quadratic Regulator (LQR) control theory, weights are optimized by modeling structural behavior while striving to minimize floor displacement and acceleration. The experimental setup involved a four-story shear structure outfitted with position sensors and accelerometers, which fed data into sensor nodes. These nodes relayed information to control nodes that calculated the necessary control force, which was then transmitted to actuator carts, ensuring effective stabilization of the structure