The Transient Predictor
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Author / Producer
Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
This paper introduces the Transient Predictor and describes how it can be used to estimate the Multistep Predictor, which can be applied to applications such as Data-Driven Predictive Control (DDPC). The Transient Predictor has two desirable traits that differentiate it from other methods for estimating the Multistep Predictor, such as the standard Subspace Predictor method:
1) Causality---the Transient Predictor asserts a causal relationship between future inputs and future outputs; and
2) Bias---the Transient Predictor is a consistent predictor of future outputs.
This paper provides an easy-to-implement algorithm for estimating the Transient Predictor and in turn the Multistep Predictor, and demonstrates its efficacy for DDPC. In experiments, we find that the Transient Predictor-based DDPC performs remarkably well with small lead-in data lengths, indicating that it is well-suited for tasks in which large amounts of data are not available. In addition, the Transient Predictor is not afflicted by the same bias as subspace-based methods when data is gathered in closed loop.
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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.
1871 - 1876
Publisher
IEEE
Event
63rd IEEE Conference on Decision and Control (CDC 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09478 - Dörfler, Florian / Dörfler, Florian
Notes
Conference lecture held on December 16, 2024
Funding
180545 - NCCR Automation (phase I) (SNF)