On data-driven stochastic output-feedback predictive control

Journal
IEEE transactions on automatic control  
Citation
IEEE Transactions on Automatic Control (in Press): (2024)
Publisher DOI
10.1109/TAC.2024.3494394
Scopus ID
2-s2.0-85209224963
Publisher
IEEE
The fundamental lemma by Jan C. Willems and co-authors enables the representation of all input-output trajectories of a linear time-invariant system by measured input-output data. This result has proven to be pivotal for data-driven control. Building on a stochastic variant of the fundamental lemma, this paper presents a data-driven output-feedback predictive control scheme for stochastic Linear Time-Invariant (LTI) systems. The considered LTI systems are subject to non-Gaussian disturbances about which only information about their first two moments is known. Leveraging polynomial chaos expansions, the proposed scheme is centered around a data-driven stochastic Optimal Control Problem (OCP). Through tailored online design of initial conditions, we provide sufficient conditions for the recursive feasibility of the proposed output-feedback scheme based on a data-driven design of the terminal ingredients of the OCP. Furthermore, we provide a robustness analysis of the closed-loop performance. A numerical example illustrates the efficacy of the proposed scheme.
Subjects
Data-driven control | model predictive control | output feedback | recursive feasibility | stochastic MPC | Willems' fundamental lemma | Witsenhausen
DDC Class
600: Technology