Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems
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 becau...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153783 https://orcid.org/0009-0005-6465-4833 |
| _version_ | 1826189077525823488 |
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| author | Zhang, Songyuan |
| author2 | Fan, Chuchu |
| author_facet | Fan, Chuchu Zhang, Songyuan |
| author_sort | Zhang, Songyuan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:09:11Z |
| format | Thesis |
| id | mit-1721.1/153783 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:09:11Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1537832024-03-16T04:05:50Z Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems Zhang, Songyuan Fan, Chuchu Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 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. S.M. 2024-03-15T19:23:46Z 2024-03-15T19:23:46Z 2024-02 2024-02-16T20:56:50.945Z Thesis https://hdl.handle.net/1721.1/153783 https://orcid.org/0009-0005-6465-4833 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Zhang, Songyuan Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems |
| title | Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems |
| title_full | Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems |
| title_fullStr | Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems |
| title_full_unstemmed | Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems |
| title_short | Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems |
| title_sort | learning stabilizing controllers for high dimensional unknown systems and networked dynamical systems |
| url | https://hdl.handle.net/1721.1/153783 https://orcid.org/0009-0005-6465-4833 |
| work_keys_str_mv | AT zhangsongyuan learningstabilizingcontrollersforhighdimensionalunknownsystemsandnetworkeddynamicalsystems |