Learning the Dynamics of Autonomous Linear Systems From Multiple Trajectories

TL;DR

This paper develops finite sample guarantees for learning the dynamics of autonomous linear systems from multiple short trajectories, applicable to both stable and unstable systems, with rates depending on trajectory length and stability.

Contribution

It introduces a novel analysis for learning system dynamics from multiple short trajectories, extending results to unstable and marginally stable systems.

Findings

Achieves a learning rate of O(1/√N) for both stable and unstable systems.

Provides trajectory length adjustments for desired learning rates in stable and marginally stable systems.

Extends existing results to scenarios without steady state observations.

Abstract

We consider the problem of learning the dynamics of autonomous linear systems (i.e., systems that are not affected by external control inputs) from observations of multiple trajectories of those systems, with finite sample guarantees. Existing results on learning rate and consistency of autonomous linear system identification rely on observations of steady state behaviors from a single long trajectory, and are not applicable to unstable systems. In contrast, we consider the scenario of learning system dynamics based on multiple short trajectories, where there are no easily observed steady state behaviors. We provide a finite sample analysis, which shows that the dynamics can be learned at a rate $\mathcal{O}(\frac{1}{\sqrt{N}})$ for both stable and unstable systems, where $N$ is the number of trajectories, when the initial state of the system has zero mean (which is a common assumption in the existing literature). We further generalize our result to the case where the initial state has non-zero mean. We show that one can adjust the length of the trajectories to achieve a learning rate of $\mathcal{O}(\sqrt{\frac{\log{N}}{N})}$ for strictly stable systems and a learning rate of $\mathcal{O}(\frac{(\log{N})^d}{\sqrt{N}})$ for marginally stable systems, where $d$ is some constant.