GA-optimized fuzzy logic control of a large-scale building for seismic loads

Validation of fuzzy logic controllers that are optimized by a genetic algorithm is pursued in this study. Fuzzy logic controllers are designed to manage two 20 kN magnetorheological dampers for mitigation of seismic loads applied to a 9 m tall, three-story steel frame benchmark building. In order to develop a set of robust controllers that are sensitive to a variety of excitations, a genetic algorithm that considers multiple objectives concurrently is proposed. Four optimization objectives have been selected which necessitates employment of a controlled elitist genetic algorithm. Optimal controllers are identified and validated through numerical simulation and full-scale experimental shake table tests for a variety of seismic excitations. Furthermore, a modified version of the same genetic algorithm is used to identify a state-space representation of the benchmark structure. Results show that optimized fuzzy logic controllers are robust and effective in reduction of both displacement and acceleration responses for both near- and far-field seismic events.