The shortest experiment for linear system identification

TL;DR

This paper introduces an online experiment design method for linear system identification that achieves the shortest possible experiments, outperforming existing offline and online methods in sample efficiency.

Contribution

The paper presents a novel iterative online input design approach that guarantees minimal experiment length for linear system identification.

Findings

Outperforms offline persistency of excitation methods

Reduces experiment length compared to existing online methods

Achieves optimal sample complexity for system identification

Abstract

This paper is concerned with the following problem: given an upper bound of the state-space dimension and lag of a linear time-invariant system, design a sequence of inputs so that the system dynamics can be recovered from the resulting input-output data. As our main result we propose a new online experiment design method, meaning that the selection of the inputs is iterative and guided by data samples collected in the past. We show that this approach leads to the shortest possible experiments for linear system identification. In terms of sample complexity, the proposed method outperforms offline methods based on persistency of excitation as well as existing online experiment design methods.