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Data-driven stochastic output-feedback predictive control : recursive feasibility through interpolated initial conditions
Publikationstyp
Conference Paper
Date Issued
2023-01-01
Sprache
English
Author(s)
Volume
211
Start Page
980
End Page
992
Citation
5th Annual Conference on Learning for Dynamics and Control, L4DC (2023)
Contribution to Conference
Scopus ID
Publisher
ML Research Press
This paper investigates data-driven output-feedback predictive control of linear systems subject to stochastic disturbances. The scheme relies on the recursive solution of a suitable data-driven reformulation of a stochastic Optimal Control Problem (OCP), which allows for forward prediction and optimization of statistical distributions of inputs and outputs. Our approach avoids the use of parametric system models. Instead it is based on previously recorded data and on a recently proposed stochastic variant of Willems' fundamental lemma. The stochastic variant of the lemma is applicable to linear dynamics subject to a large class of stochastic disturbances of Gaussian or non-Gaussian nature. To ensure recursive feasibility, the initial condition of the OCP-which consists of information about past inputs and outputs-is considered as an extra decision variable of the OCP. We provide sufficient conditions for recursive feasibility of the proposed scheme as well as a bound on the asymptotic average performance. Finally, a numerical example illustrates the efficacy and the closed-loop properties of the proposed scheme.
Subjects
Data-driven control
stochastic predictive control
Willems' fundamental lemma
DDC Class
510: Mathematics