{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/153783"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/153783","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems","abstract":"Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations because of the scalability and model transparency, and large-scale networked dynamical systems because of scalability and generalizability. To address the challenge, we develop (1) A Lyapunov-based guided exploration framework to learn stabilizing controllers for high-dimensional unknown systems; (2) A compositional neural certificate based on ISS (Input-to-State Stability) Lyapunov functions for finding decentralized stabilizing controllers in large-scale networked dynamical systems. Comprehensive experiments have shown that the proposed methods outperform the prior work in the case of stability, especially in high-dimensional unknown systems and large-scale networked systems.","abstract_html":"Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations because of the scalability and model transparency, and large-scale networked dynamical systems because of scalability and generalizability. To address the challenge, we develop (1) A Lyapunov-based guided exploration framework to learn stabilizing controllers for high-dimensional unknown systems; (2) A compositional neural certificate based on ISS (Input-to-State Stability) Lyapunov functions for finding decentralized stabilizing controllers in large-scale networked dynamical systems. Comprehensive experiments have shown that the proposed methods outperform the prior work in the case of stability, especially in high-dimensional unknown systems and large-scale networked systems.","abstract_has_math":false,"creators":["Zhang, Songyuan"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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To address the challenge, we develop (1) A Lyapunov-based guided exploration framework to learn stabilizing controllers for high-dimensional unknown systems; (2) A compositional neural certificate based on ISS (Input-to-State Stability) Lyapunov functions for finding decentralized stabilizing controllers in large-scale networked dynamical systems. Comprehensive experiments have shown that the proposed methods outperform the prior work in the case of stability, especially in high-dimensional unknown systems and large-scale networked systems."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Fan, Chuchu"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Aeronautics and Astronautics"],"dc:creator":["Zhang, Songyuan"],"dc:date.accessioned":["2024-03-15T19:23:46Z"],"dc:date.available":["2024-03-15T19:23:46Z"],"dc:date.issued":["2024-02"],"dc:description.abstract":["Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations because of the scalability and model transparency, and large-scale networked dynamical systems because of scalability and generalizability. To address the challenge, we develop (1) A Lyapunov-based guided exploration framework to learn stabilizing controllers for high-dimensional unknown systems; (2) A compositional neural certificate based on ISS (Input-to-State Stability) Lyapunov functions for finding decentralized stabilizing controllers in large-scale networked dynamical systems. Comprehensive experiments have shown that the proposed methods outperform the prior work in the case of stability, especially in high-dimensional unknown systems and large-scale networked systems."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/153783"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Aeronautics and Astronautics"]},"updated_at":"2026-07-22T22:20:48Z"}