{"681799":{"#nid":"681799","#data":{"type":"event","title":"MS Defense by Peter Lehmann","body":[{"value":"\u003Cp\u003EPeter Lehmann\u003Cbr\u003E(Advisor: Prof. Theodorou)\u003Cbr\u003Ewill defend a master\u2019s thesis entitled,\u003Cbr\u003EMaximum Entropy Sequential Quadratic Programming\u003Cbr\u003EOn\u003Cbr\u003EMonday, April 21 at 10:00 a.m.\u0026nbsp;\u003Cbr\u003ECoda C1315 Grant Park\u0026nbsp;\u003Cbr\u003E756 W Peachtree St NW\u003Cbr\u003EAbstract\u003Cbr\u003ETrajectory optimization (TO) plays a critical role across a broad spectrum of scientific and engineering fields, including robotics, energy and power systems, economics, and biomechanics. Through the lens of optimal control, TO often involves solving constrained optimization problems with non-convex objective function, nonlinear state and actuation constraints, and nonlinear dynamics. A widely used technique to solve such problems is Sequential Quadratic Programming (SQP). This method iteratively solves a series of quadratic subproblems, each of which relies on first- and second-order approximations of the constraints and cost around a nominal trajectory. However, since each subproblem only captures local information, SQP can suffer from converging to local minima. To overcome this limitation, this thesis proposes Maximum Entropy Sequential Quadratic Programming (MESQP). The proposed method leverages a stochastic policy and introduces an entropy-based regularization into the objective function. This regularization encourages exploration during optimization and enables the formulation of MESQP under both unimodal and multimodal policy representations. By embedding this stochastic policy into the SQP framework, the resulting MESQP algorithm creates a scheme of alternating optimization and sampling steps, yielding the ability to overcome local minima. To evaluate the method\u2019s efficacy, this framework is compared experimentally with regular SQP and Maximum Entropy Differential Dynamic Programming (MEDDP) across several TO tasks.\u003Cbr\u003ECommittee\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Prof. Evangelos Theodorou \u2013 School of Aerospace Engineering (advisor)\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Prof. Kyriakos Vamvoudakis \u2013 School of Aerospace Engineering\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Prof. Lu Gan \u2013 School of Aerospace Engineering\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EMaximum Entropy Sequential Quadratic Programming\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Maximum Entropy Sequential Quadratic Programming"}],"uid":"27707","created_gmt":"2025-04-15 17:49:42","changed_gmt":"2025-04-15 17:50:16","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-21T10:00:00-04:00","event_time_end":"2025-04-21T12:00:00-04:00","event_time_end_last":"2025-04-21T12:00:00-04:00","gmt_time_start":"2025-04-21 14:00:00","gmt_time_end":"2025-04-21 16:00:00","gmt_time_end_last":"2025-04-21 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Coda C1315 Grant Park  756 W Peachtree St NW","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"111531","name":"ms defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}