Online Adaptive Neurochaotic Fuzzy Controller Design to Reduce the Seismic Response of Buildings Equipped with Active Tuned Mass Damper System

Abstract

This paper presents a novel adaptive neurochaotic fuzzy control system based on type-2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers under near-field and far-field earthquakes. In this proposed control system, the whole parameters of the plant are assumed to be completely unknown, the structural model is estimated using a multilayer perceptron neural network, and the system’s Jacobian is extracted. The online estimation model is used, and the controller parameters are adaptively trained using the extended Kalman filter and error back-propagation method. Subsequently, the control force is applied to the active tuned mass damper, and the control objectives are met. The adaptive controller does not require initial settings, and a fractional-order proportional-integral-derivative controller is added to maximize stability and robustness against seismic vibration. A simple adaptive controller optimized by a particle swarm is also presented as an innovation. Comparing the performance of the improved simple adaptive controller and adaptive neurochaotic fuzzy controller, the proposed controllers appear more efficient and accurate. However, the superiority of the novel adaptive neurochaotic fuzzy over the improved simple adaptive controller in reducing maximum displacement, acceleration, drift, and base shear while maintaining acceptable performance under parametric uncertainties and seismic conditions is substantial.