{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83505"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83505","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Active Control of Structures Using Genetic Algorithms","abstract":"The methodology developed three major design aspects: a genetic algorithm-based control parameter optimization based on the real design criteria, a state space reconstruction technique for estimating the full state of a structure using accelerations measured from sensors installed on the structure, and the robustness of controllers to sensor noise and model uncertainty. These three topics are investigated in this study. Controllers are designed using different robustness criteria, and the results are compared and discussed. Results are also examined with other well-known control methods on the same evaluation criteria to provide a comparison. The proposed methodology has great potential to develop a general control design environment for various controllers including neuro and fuzzy controllers.","abstract_html":"The methodology developed three major design aspects: a genetic algorithm-based control parameter optimization based on the real design criteria, a state space reconstruction technique for estimating the full state of a structure using accelerations measured from sensors installed on the structure, and the robustness of controllers to sensor noise and model uncertainty. These three topics are investigated in this study. Controllers are designed using different robustness criteria, and the results are compared and discussed. Results are also examined with other well-known control methods on the same evaluation criteria to provide a comparison. 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These three topics are investigated in this study. Controllers are designed using different robustness criteria, and the results are compared and discussed. Results are also examined with other well-known control methods on the same evaluation criteria to provide a comparison. The proposed methodology has great potential to develop a general control design environment for various controllers including neuro and fuzzy controllers.","Made available in DSpace on 2015-09-25T21:05:19Z (GMT). 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These three topics are investigated in this study. Controllers are designed using different robustness criteria, and the results are compared and discussed. Results are also examined with other well-known control methods on the same evaluation criteria to provide a comparison. The proposed methodology has great potential to develop a general control design environment for various controllers including neuro and fuzzy controllers.","Made available in DSpace on 2015-09-25T21:05:19Z (GMT). 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