Control of a benchmark structure using GA-optimized fuzzy logic control
MetadataShow full item record
Mitigation of displacement and acceleration responses of a three story benchmark structure excited by seismic motions is pursued in this study. Multiple 20-kN magnetorheological (MR) dampers are installed in the three-story benchmark structure and managed by a global fuzzy logic controller to provide smart damping forces to the benchmark structure. Two configurations of MR damper locations are considered to display multiple-input, single-output and multiple-input, multiple-output control capabilities. Characterization tests of each MR damper are performed in a laboratory to enable the formulation of fuzzy inference models. Prediction of MR damper forces by the fuzzy models shows sufficient agreement with experimental results. A controlled-elitist multi-objective genetic algorithm is utilized to optimize a set of fuzzy logic controllers with concurrent consideration to four structural response metrics. The genetic algorithm is able to identify optimal passive cases for MR damper operation, and then further improve their performance by intelligently modulating the command voltage for concurrent reductions of displacement and acceleration responses. An optimal controller is identified and validated through numerical simulation and fullscale experimentation. Numerical and experimental results show that performance of the controller algorithm is superior to optimal passive cases in 43% of investigated studies. Furthermore, the state-space model of the benchmark structure that is used in numerical simulations has been improved by a modified version of the same genetic algorithm used in development of fuzzy logic controllers. Experimental validation shows that the state-space model optimized by the genetic algorithm provides accurate prediction of response of the benchmark structure to base excitation.
fuzzy logic control
acceleration feedback control
multiobjective genetic algorithm
Shook, David Adam (2006). Control of a benchmark structure using GA-optimized fuzzy logic control. Master's thesis, Texas A&M University. Available electronically from
Showing items related by title, author, creator and subject.
Langari, R. (Energy Systems Laboratory (http://esl.tamu.edu), 1997-04)In this paper an approach to supervisory control of multi-stage industrial control systems is presented. This approach is based on the notion of an internal reference model, and further makes use of a fuzzy multi-objective ...
Mellit, A.; Benghanme, M.; Arab, A. H.; Guessoum, A. (Energy Systems Laboratory (http://esl.tamu.edu)Texas A&M University (http://www.tamu.edu), 2004)With industrial development the problem of energy shortage is more and more aggravating. The photovoltaic (PV) systems are rapidly expanding and have increasing in electric power technology and regarded as the green energy ...
Recent VOC Control Test Data for a Reactive VOC Converter- Scrubber System for Non-Thermal Control of VOCs McGinness, M. (Energy Systems Laboratory (http://esl.tamu.edu), 2003-05)HAP (Hazardous Air Pollutant) and VOC (Volatile Organic Compound) thermal emission control devices (ECD) usually require large amounts of energy to operate. They also require large capital investments in heat recovery ...