Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Selected papers from the IEEE/Nagoya-University World Wisepersons Workshop on Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms,
Active control of earthquake excited structures using fuzzy supervisory technique
Advances in Engineering Software
Active control of building structures using piezoelectric actuators
Applied Soft Computing
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The effectiveness of a supervisory fuzzy control technique for reduction of seismic response of a smart base isolation system is investigated in this study. To this end, a first generation, base isolated, benchmark building is employed for numerical simulation. The benchmark structure under consideration has eight stories and an irregular plan. Furthermore it is equipped with low damping elastomeric bearings and magnetorheological (MR) dampers for seismic protection. The proposed control technique employs a hierarchical structure of fuzzy logic controllers (FLC) consisting of two lower-level controllers (sub-FLC) and a higher-level supervisory controller. One sub-FLC has been optimized for near-fault earthquakes and the other sub-FLC is well-suited for far-fault earthquakes. These sub-FLCs are optimized by use of a multi-objective genetic algorithm. Four objectives, i.e. reduction of peak superstructure acceleration, peak isolation system deformation, RMS superstructure acceleration and RMS isolation system deformation are used in a multi-objective optimization process. When an earthquake is applied to the benchmark building, each of the sub-FLCs provides different command voltages for the semi-active controllers and the supervisory fuzzy controller appropriately combines the two command voltages based on a fuzzy inference system in real time. Results from numerical simulations demonstrate that isolation system deformation as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique in comparison with a sample clipped optimal controller.