{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/55127"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/55127","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"Real-Time Trajectory Optimization for High-Performance Guidance & Control","abstract":"Autonomous systems of today rely on trajectory planning to achieve complex tasks. With the increasing capabilities of such systems, there is a need for a framework that not only allows for accurate modeling of these tasks, but also enables real-time generation of feasible trajectories to achieve them. This dissertation presents trajectory generation methods, using gradient-based optimization and set-based dynamic programming, for a large class of optimal, robust, and resilient control problems. These methods are intended for adoption onboard agile autonomous systems—such as reusable rockets—that mandate high-performance guidance & control.","abstract_html":"Autonomous systems of today rely on trajectory planning to achieve complex tasks. With the increasing capabilities of such systems, there is a need for a framework that not only allows for accurate modeling of these tasks, but also enables real-time generation of feasible trajectories to achieve them. This dissertation presents trajectory generation methods, using gradient-based optimization and set-based dynamic programming, for a large class of optimal, robust, and resilient control problems. These methods are intended for adoption onboard agile autonomous systems—such as reusable rockets—that mandate high-performance guidance &amp; control.","abstract_has_math":false,"creators":["Kamath, Abhinav Girish"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Açıkmeşe, Behçet"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-02-05","date_published":"2026-02-05","updated_at":"2026-07-24T05:58:05Z","subjects":["Aerospace engineering"],"languages":["en_US"],"rights":["none"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1773/55127","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Açıkmeşe, Behçet"]},{"key":"dc:creator","label":"Author","values":["Kamath, Abhinav Girish"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-05T19:30:24Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-05T19:30:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-02-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Aerospace engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["none"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["Kamath_washington_0250E_29127.pdf"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1773/55127"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (Ph.D.)--University of Washington, 2025"]},{"key":"dc:description.abstract","label":"Abstract","values":["Autonomous systems of today rely on trajectory planning to achieve complex tasks. With the increasing capabilities of such systems, there is a need for a framework that not only allows for accurate modeling of these tasks, but also enables real-time generation of feasible trajectories to achieve them. This dissertation presents trajectory generation methods, using gradient-based optimization and set-based dynamic programming, for a large class of optimal, robust, and resilient control problems. These methods are intended for adoption onboard agile autonomous systems—such as reusable rockets—that mandate high-performance guidance & control."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Real-Time Trajectory Optimization for High-Performance Guidance & Control"]}]}],"canonical_facts":{"dc:contributor.advisor":["Açıkmeşe, Behçet"],"dc:creator":["Kamath, Abhinav Girish"],"dc:date.accessioned":["2026-02-05T19:30:24Z"],"dc:date.available":["2026-02-05T19:30:24Z"],"dc:date.issued":["2026-02-05"],"dc:description":["Thesis (Ph.D.)--University of Washington, 2025"],"dc:description.abstract":["Autonomous systems of today rely on trajectory planning to achieve complex tasks. With the increasing capabilities of such systems, there is a need for a framework that not only allows for accurate modeling of these tasks, but also enables real-time generation of feasible trajectories to achieve them. This dissertation presents trajectory generation methods, using gradient-based optimization and set-based dynamic programming, for a large class of optimal, robust, and resilient control problems. These methods are intended for adoption onboard agile autonomous systems—such as reusable rockets—that mandate high-performance guidance & control."],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["Kamath_washington_0250E_29127.pdf"],"dc:identifier.uri":["https://hdl.handle.net/1773/55127"],"dc:language.iso":["en_US"],"dc:rights":["none"],"dc:subject":["Aerospace engineering"],"dc:title":["Real-Time Trajectory Optimization for High-Performance Guidance & Control"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T05:58:05Z"}