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Poster
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory
Yang Hu · Adam Wierman · Guannan Qu

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #936
in Poster Session 4 »
Stabilizing an unknown dynamical system is one of the central problems in control theory. In this paper, we study the sample complexity of the learn-to-stabilize problem in Linear Time-Invariant (LTI) systems on a single trajectory. Current state-of-the-art approaches require a sample complexity linear in $n$, the state dimension, which incurs a state norm that blows up exponentially in $n$. We propose a novel algorithm based on spectral decomposition that only needs to learn ``a small part'' of the dynamical matrix acting on its unstable subspace. We show that, under proper assumptions, our algorithm stabilizes an LTI system on a single trajectory with $O(k \log n)$ samples, where $k$ is the instability index of the system. This represents the first sub-linear sample complexity result for the stabilization of LTI systems under the regime when $k = o(n)$.
Keywords: Sample Complexity · Stability · linear time-invariant systems · learning-based control

Author Information

Yang Hu (SEAS, Harvard University)
Adam Wierman (Caltech)
Guannan Qu (Carnegie Mellon University)

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