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Sampling-Based Stochastic Data-Driven Predictive Control Under Data Uncertainty
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DOI：10.1109/TAC.2025.3617610.png)
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Abstract
En 中文
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 PE 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.
Keywords:
Data-driven control
predictive control
stochastic systems
chance constraints
sampling
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W