Offline Uncertainty Sampling in Data-driven Stochastic MPC

• Published in: IFAC-PapersOnLine Vol. 56; no. 2; pp. 650 - 656
• Main Authors: Teutsch, Johannes, Kerz, Sebastian, Brüdigam, Tim, Wollherr, Dirk, Leibold, Marion
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2023
• Subjects: Constrained control; Data-based control; Data-driven optimal control; Linear systems; Predictive control; Stochastic optimal control problems; Uncertain systems; Linear systems; Uncertain systems; Data-driven optimal control; Data-based control; Constrained control; Stochastic optimal control problems; Predictive control
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2023.10.1641

In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The strategy uses only past measured, potentially noisy data in a non-parametric system representation and does not require any prior model identification. The approximation of chance constraints using uncertainty sampling leads to efficient constraint tightening. Under mild assumptions, robust recursive feasibility and closed-loop constraint satisfaction is shown. In a simulation example, we provide evidence for the improved control performance of the proposed control scheme in comparison to a purely robust data-driven predictive control approach.