{"593554":{"#nid":"593554","#data":{"type":"event","title":"Ph.D. Thesis Defense:  Matthew Gross","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EPh.D. Thesis Defense by\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Ch2\u003E\u003Cstrong\u003EMatthew Gross\u003C\/strong\u003E\u003C\/h2\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdvisor: Dr. Mark Costello\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Ch2\u003E\u003Cstrong\u003E\u0026ldquo;Smart Projectile Parameter Estimation Using \u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cstrong\u003EMeta-Optimization\u0026rdquo;\u003C\/strong\u003E\u003C\/h2\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMonday, July 31, 2017 @ 1 p.m.\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cstrong\u003EMontgomery-Knight Room 317\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cdiv\u003E\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; System identification and parameter estimation are valuable tools in the analysis and design of smart projectile systems.\u0026nbsp; Given the complexity of these systems, it is convenient to work with mathematical models in place of the actual system.\u0026nbsp; Parameter estimation uses time history data of the system to determine a model that accurately matches the data.\u0026nbsp; Many techniques have been developed to perform parameter estimation, including regression methods, maximum likelihood estimators, and Kalman filters.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Maximum likelihood methods, in particular the output error method (OEM), pose the estimation problem in terms of an optimization problem.\u0026nbsp; OEM has seen extensive use on projectile systems, utilizing a numerical optimizer such as a Newton style algorithm to solve for unknown parameters.\u0026nbsp; These algorithms are prone to converging on local minima present in the projectile dynamics, requiring reasonable initial guesses of the parameters to ensure convergence.\u0026nbsp; However, for new smart projectile systems, prior estimates of the control parameters may not be available.\u0026nbsp; Thus, there is a need for reliable and robust parameter estimation methods that are not dependent a priori knowledge of the parameters.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; This thesis proposes a new method for smart projectile parameter estimation based on OEM.\u0026nbsp; To achieve robust and reliable parameter estimates, a new underlying optimization algorithm is formed dubbed meta-optimization.\u0026nbsp; Meta-optimization employs a diverse set of individual optimization algorithms with both local and global search capabilities.\u0026nbsp; The meta-optimizer operates by iteratively selecting a single algorithm to deploy in a stochastic manner, giving preference to algorithms which have performed well on the problem.\u0026nbsp; This approach allows synergies to develop between the individual optimizers, boosting performance beyond what each optimizer is capable of individually.\u0026nbsp; A suite of benchmark functions are used to analyze the meta-optimization framework and compare it to other existing algorithms.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; The new parameter estimation method is applied to an example smart projectile system equipped with a new control mechanism.\u0026nbsp; Both synthetic and experimental trajectory data is used to evaluate the effective of the proposed method.\u0026nbsp; For the standard projectile and a smart projectile executing a maneuver, the method obtains good estimates of the parameters for this system in the presence of measurement noise.\u003C\/p\u003E\r\n\u003C\/div\u003E\r\n\r\n\u003Cdiv\u003E\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee Members\u003C\/strong\u003E\u003Cbr \/\u003E\r\nDr. Mark Costello, AE (Advisor)\u003Cbr \/\u003E\r\nDr. Brian German, AE\u003Cbr \/\u003E\r\nDr. Eric Johnson, AE\u003Cbr \/\u003E\r\nDr. Graeme Kennedy, AE\u003Cbr \/\u003E\r\nDr. Aldo Ferri, ME\u003C\/p\u003E\r\n\u003C\/div\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"\u201cSmart Projectile Parameter Estimation Using  Meta-Optimization\u201d"}],"uid":"33975","created_gmt":"2017-07-18 12:58:32","changed_gmt":"2017-07-18 17:22:49","author":"Margaret Ojala","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2017-07-31T14:00:00-04:00","event_time_end":"2017-07-31T16:00:00-04:00","event_time_end_last":"2017-07-31T16:00:00-04:00","gmt_time_start":"2017-07-31 18:00:00","gmt_time_end":"2017-07-31 20:00:00","gmt_time_end_last":"2017-07-31 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1239","name":"School of Aerospace Engineering"}],"categories":[],"keywords":[{"id":"2082","name":"aerospace engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}