Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty - Extended Version
Abstract
We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundamental lemma and requires only a single persistently exciting input-output data trajectory. Compared to current state-of-the-art approaches, we do not rely on availability of exact disturbance data. Instead, we leverage a novel parameterization of the unknown disturbance data considering consistency with the measured data and the system class. This allows for deterministic approximation of the chance constraints in a sampling-based fashion. A robust constraint on the first predicted step enables recursive feasibility, closed-loop constraint satisfaction, and robust asymptotic stability in expectation under standard assumptions. A numerical example demonstrates the efficiency of the proposed control scheme.
Cite
@article{arxiv.2402.00681,
title = {Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty - Extended Version},
author = {Johannes Teutsch and Sebastian Kerz and Dirk Wollherr and Marion Leibold},
journal= {arXiv preprint arXiv:2402.00681},
year = {2025}
}