Learning Stabilizing Controllers for High-dimensional Unknown Systems and Networked Dynamical Systems
Name
Zhang-szhang21-sm-aeroastro-2024-thesis.pdf
Description
Thesis PDF
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4.05 MB
Format
Adobe PDF
Checksum (MD5)
b492d3027b4cc9b4395345d29eca2598
Author(s)
Zhang, Songyuan
Advisor(s)
Fan, Chuchu
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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