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Bibliographic Details
Main Authors: Abdalmoaty, Mohamed, Smith, Roy S.
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.07907
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author Abdalmoaty, Mohamed
Smith, Roy S.
author_facet Abdalmoaty, Mohamed
Smith, Roy S.
contents A recently developed data-driven Kalman filter requires offline measurement of the process disturbance; a requirement that is often unmet for many practical applications. We propose a solution that parametrizes the Kalman filter exclusively using measured input and output data. The key idea is to use the innovations form which naturally accounts for the process disturbance and measurement noise into a single orthogonal stochastic process. Unlike process disturbances, the innovations process can be estimated directly from input-output data via a numerically efficient projection step. The performance of the method is demonstrated using a benchmark simulation.
format Preprint
id arxiv_https___arxiv_org_abs_2511_07907
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle An Innovations-Based Data-Driven Kalman Predictor for Predictive Control
Abdalmoaty, Mohamed
Smith, Roy S.
Systems and Control
A recently developed data-driven Kalman filter requires offline measurement of the process disturbance; a requirement that is often unmet for many practical applications. We propose a solution that parametrizes the Kalman filter exclusively using measured input and output data. The key idea is to use the innovations form which naturally accounts for the process disturbance and measurement noise into a single orthogonal stochastic process. Unlike process disturbances, the innovations process can be estimated directly from input-output data via a numerically efficient projection step. The performance of the method is demonstrated using a benchmark simulation.
title An Innovations-Based Data-Driven Kalman Predictor for Predictive Control
topic Systems and Control
url https://arxiv.org/abs/2511.07907