{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:osu1364242984"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:osu1364242984","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"Fuzzy Model Reference Learning Control for Smart Lights","abstract":"This research is motivated by the world's fast-growing demand for energy savings. Due to the increasing cost of fossil fuels (e.g., oil, coal, natural gas), research has been conducted to effectively reduce the electricity consumption in office buildings by means of employing smart lighting. This thesis investigates the implementation of an adaptive and nonadaptive fuzzy control for a smart light experimental testbed. The objective is to accurately regulate the light level across the experimental testbed to a desired voltage reference value, and to test the performance of the fuzzy controllers under cross-illumination effects, and bulb and sensor failures. As an initial approach, a decentralized (i.e., no communication between controllers) nonadaptive fuzzy controller is implemented and applied to the experimental testbed. This approach is convenient for this type of experimental testbed where a mathematical model of the plant is not available and heuristic information about how to control the system is sufficient. The nonadaptive fuzzy controller, when properly tuned, is able to achieve uniform lighting across the entire testbed floor in most of the tested situations but it fails whenever an on/off light bulb failure is introduced. In order to attain uniform lighting for complex failures, a fuzzy model reference learning controller (i.e., adaptive fuzzy) is developed for the experimental testbed, and this algorithm proves to be able to adapt to uncertainties such as disturbances and failures via a learning mechanism.","abstract_html":"This research is motivated by the world's fast-growing demand for energy savings. Due to the increasing cost of fossil fuels (e.g., oil, coal, natural gas), research has been conducted to effectively reduce the electricity consumption in office buildings by means of employing smart lighting. This thesis investigates the implementation of an adaptive and nonadaptive fuzzy control for a smart light experimental testbed. The objective is to accurately regulate the light level across the experimental testbed to a desired voltage reference value, and to test the performance of the fuzzy controllers under cross-illumination effects, and bulb and sensor failures. As an initial approach, a decentralized (i.e., no communication between controllers) nonadaptive fuzzy controller is implemented and applied to the experimental testbed. This approach is convenient for this type of experimental testbed where a mathematical model of the plant is not available and heuristic information about how to control the system is sufficient. The nonadaptive fuzzy controller, when properly tuned, is able to achieve uniform lighting across the entire testbed floor in most of the tested situations but it fails whenever an on/off light bulb failure is introduced. In order to attain uniform lighting for complex failures, a fuzzy model reference learning controller (i.e., adaptive fuzzy) is developed for the experimental testbed, and this algorithm proves to be able to adapt to uncertainties such as disturbances and failures via a learning mechanism.","abstract_has_math":false,"creators":["Velasquez Garrido, Jose J."],"institution":"The Ohio State University","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Passino, Kevin M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-06-17","date_published":"2013-06-17","updated_at":"2026-07-24T03:37:01Z","subjects":["Electrical Engineering","adaptive algorithm","adaptive fuzzy control","distributed control","lighting control","fuzzy control","smart lights system"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. 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The objective is to accurately regulate the light level across the experimental testbed to a desired voltage reference value, and to test the performance of the fuzzy controllers under cross-illumination effects, and bulb and sensor failures. As an initial approach, a decentralized (i.e., no communication between controllers) nonadaptive fuzzy controller is implemented and applied to the experimental testbed. This approach is convenient for this type of experimental testbed where a mathematical model of the plant is not available and heuristic information about how to control the system is sufficient. The nonadaptive fuzzy controller, when properly tuned, is able to achieve uniform lighting across the entire testbed floor in most of the tested situations but it fails whenever an on/off light bulb failure is introduced. In order to attain uniform lighting for complex failures, a fuzzy model reference learning controller (i.e., adaptive fuzzy) is developed for the experimental testbed, and this algorithm proves to be able to adapt to uncertainties such as disturbances and failures via a learning mechanism."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.153","10.58 MB"]},{"key":"dc:title","label":"Title","values":["Fuzzy Model Reference Learning Control for Smart Lights"]}]}],"canonical_facts":{"dc:contributor":["Passino, Kevin M."],"dc:creator":["Velasquez Garrido, Jose J."],"dc:date":["2013-06-17"],"dc:description":["This research is motivated by the world's fast-growing demand for energy savings. Due to the increasing cost of fossil fuels (e.g., oil, coal, natural gas), research has been conducted to effectively reduce the electricity consumption in office buildings by means of employing smart lighting. 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