Indirect data-driven predictive control and the state-space predictor

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Levi D. Reyes Premer, Arash J. Khabbazi, Kevin J. Kircher
DOI: 10.48550/arxiv.2602.10936
Cited by: 0

Abstract

We define trajectory predictive control (TPC) as a class of indirect data-driven predictive control (DDPC) methods that represent future outputs as linear in past inputs/outputs and future inputs. TPC unifies many DDPC variants with different predictor structures. We introduce a predictor with a state-space representation and show that with it, TPC inherits the mature theory of linear model predictive control. In numerical experiments, the state-space predictor outperforms existing predictors, especially for small training datasets.

Fields of Study & Subjects

Advanced Control Systems Optimization Control Systems and Identification Iterative Learning Control Systems

Publication details

Journal / source
arXiv (Cornell University)
Publication date
11 February 2026
Language
EN
Document type
Preprint