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arXiv · 2402.00681

Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty - Extended Version

Johannes Teutsch·Sebastian Kerz·Dirk Wollherr·Marion Leibold
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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.

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Johannes Teutsch, Sebastian Kerz, Dirk Wollherr, Marion Leibold. 2024-02-01. Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty - Extended Version. https://doi.org/10.1109/tac.2025.3617610

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