{"629731":{"#nid":"629731","#data":{"type":"event","title":"Phd Defense by Georgios Boutselis","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EPh.D. Thesis Announcement\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBy\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgios Boutselis\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(Advisor: Prof. Evangelos Theodorou)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E4:00 PM, Monday 09 Dec 2019\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EKnight Building, Conference room 317\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EOPTIMIZATION-BASED METHODS FOR DETERMINISTIC AND STOCHASTIC CONTROL:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EALGORITHMIC DEVELOPMENT, ANALYSIS AND\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAPPLICATIONS ON MECHANICAL SYSTEMS \u0026amp; FIELDS\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESummary: \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDeveloping efficient control algorithms for practical scenarios remains a key challenge\u003C\/p\u003E\r\n\r\n\u003Cp\u003Efor the scientific community. Towards this goal, optimal control theory has been widely employed over the past decades, with applications both in simulated and real environments.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EUnfortunately, standard model-based approaches become highly ineffective when modeling\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eaccuracy degrades. This may stem from erroneous estimates of physical parameters\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(e.g., friction coefficients, moments of inertia), or dynamics components which are inherently\u003C\/p\u003E\r\n\r\n\u003Cp\u003Ehard to model. System uncertainty should therefore be properly handled within\u003C\/p\u003E\r\n\r\n\u003Cp\u003Econtrol methodologies for both theoretical and practical purposes. Of equal importance are\u003C\/p\u003E\r\n\r\n\u003Cp\u003Estate and control constraints, which must be effectively handled for safety critical systems.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETo proceed, the majority of works in controls and reinforcement learning literature\u003C\/p\u003E\r\n\r\n\u003Cp\u003Edeals with systems lying in finite-dimensional Euclidean spaces. For many interesting applications in aerospace engineering, robotics and physics, however, we must often consider\u003C\/p\u003E\r\n\r\n\u003Cp\u003Edynamics with more challenging configuration spaces. These include systems evolving\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eon differentiable manifolds, as well as systems described by stochastic partial differential\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eequations. Some problem examples of the former case are spacecraft attitude control, modeling of elastic beams and control of quantum spin systems. Regarding the latter, we have\u003C\/p\u003E\r\n\r\n\u003Cp\u003Econtrol of thermal\/fluid flows, chemical reactors and advanced batteries.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis work attempts to address the challenges mentioned above. We will develop numerical\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eoptimal control methods that explicitly incorporate modeling uncertainty, as well as deterministic and probabilistic constraints, into prediction and decision making. Our iterative\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eschemes provide scalability by relying on dynamic programming principles and sampling-based techniques. Depending upon different problem setups, we will handle uncertainty by employing suitable concepts from machine learning and uncertainty quantification theory. Moreover, we will show that well-known numerical control methods can be extended for mechanical systems evolving on manifolds, and dynamics described by stochastic partial differential equations. Our algorithmic derivations utilize key concepts from optimal control and optimization theory, and in some cases, theoretical results will be provided on the convergence properties of the proposed methods. The effectiveness and applicability of our approach will be highlighted by substantial numerical results on simulated test cases.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee Members:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProf. Evangelos Theodorou (Advisor, AE)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProf. Melvin Leok (Department of Mathematics, University of California, San Diego)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProf. Yongxin Chen (AE)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProf. Andrzej Swiech (MATH)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProf. Efstathios Bakolas (AE, The University of Texas at Austin)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":" OPTIMIZATION-BASED METHODS FOR DETERMINISTIC AND STOCHASTIC CONTROL: ALGORITHMIC DEVELOPMENT, ANALYSIS AND APPLICATIONS ON MECHANICAL SYSTEMS \u0026 FIELDS"}],"uid":"27707","created_gmt":"2019-12-05 20:43:55","changed_gmt":"2019-12-05 20:43:55","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-12-09T16:00:00-05:00","event_time_end":"2019-12-09T18:00:00-05:00","event_time_end_last":"2019-12-09T18:00:00-05:00","gmt_time_start":"2019-12-09 21:00:00","gmt_time_end":"2019-12-09 23:00:00","gmt_time_end_last":"2019-12-09 23:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"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":""}}}