{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69409"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69409","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adaptive Robust Control","abstract":"In this dissertation we present a novel approach to the problem of adaptive robust control. The essential idea involved is to fragment the modeling uncertainty in a physical plant into "small" families of plant models for which robust linear time-invariant controllers can be readily designed. We then implement a convex combination of these controllers, the parameters of which are tuned based on error measurements. 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The essential idea involved is to fragment the modeling uncertainty in a physical plant into "small" families of plant models for which robust linear time-invariant controllers can be readily designed. We then implement a convex combination of these controllers, the parameters of which are tuned based on error measurements. Using this approach, we are able to provide absolute bounds on the ability to adaptively stabilize against unmodeled dynamics.","These computations enable us to quantitatively compare nonlinear feedback with linear feedback in the context of robust stabilization problems. These results offer support to a maxim we believe holds in broad generality: the accuracy to within which a physical plant is modeled determines the type of feedback best suited to control that plant.","Made available in DSpace on 2014-12-15T19:05:39Z (GMT). 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We then implement a convex combination of these controllers, the parameters of which are tuned based on error measurements. Using this approach, we are able to provide absolute bounds on the ability to adaptively stabilize against unmodeled dynamics.","These computations enable us to quantitatively compare nonlinear feedback with linear feedback in the context of robust stabilization problems. These results offer support to a maxim we believe holds in broad generality: the accuracy to within which a physical plant is modeled determines the type of feedback best suited to control that plant.","Made available in DSpace on 2014-12-15T19:05:39Z (GMT). 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