Willems' Fundamental Lemma with Large Noisy Fragmented Dataset
This work addresses a practical limitation in data-driven control for LTI systems, offering an incremental improvement by handling noise and fragmentation in datasets.
The paper tackles the problem of applying Willems' Fundamental Lemma to large, noisy, and fragmented datasets without prior noise knowledge, resulting in a computationally efficient algorithm that estimates system invariants in seconds.
Willems' Fundamental Lemma enables parameterizing all trajectories generated by a Linear Time-Invariant (LTI) system directly from data. However, this lemma relies on the assumption of noiseless measurements. In this paper, we provide an approach that enables the applicability of Willems' Fundamental Lemma with a large noisy-input, noisy-output fragmented dataset, without requiring prior knowledge of the noise distribution. We introduce a computationally tractable and lightweight algorithm that, despite processing a large dataset, executes in the order of seconds to estimate the invariants of the underlying system, which is obscured by noise. The simulation results demonstrate the effectiveness of the proposed method.