Data-driven formulation of the Kalman filter and its Application to Predictive Control
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Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Data-driven methods for predictive control rely on input-output data to give a Hankel matrix representation of the space of trajectories. They are poorly suited to situations where both process noise and measurement noise dominate the behaviour whereas Kalman filters optimally estimate system states in this scenario. We derive a data-driven Kalman filter formulation based on the dynamic evolution of Hankel matrix output predictions. This leads to an extended state space model that describes the evolution of both the future inputs and outputs. By applying measurement feedback one arrives at a Kalman filter for the system. The Kalman filter design is performed purely on the basis of the input and output signals and without the need for a specific state-space representation. A benchmark simulation illustrates that the resulting prediction-based control significantly out-performs predictive controllers based on current data-driven methods.
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Publication status
published
Editor
Book title
2024 IEEE 63rd Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
2633 - 2639
Publisher
IEEE
Event
63rd Conference on Decision and Control (CDC 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former)
Notes
Conference lecture held on December 17, 2024.
Funding
180545 - NCCR Automation (phase I) (SNF)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000693730