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A Robust Direct Data-Driven Ellipsoidal Predictive Control Scheme
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DOI：10.1109/LCSYS.2026.3675270.png)
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Abstract
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This paper presents a Direct Data-Driven Control strategy for unknown linear systems subject to process noise. The idea is to customize a set-theoretic receding horizon controller within a data driven setting by properly converting Model-based conditions in terms of data series on the available input and state signals. The main results here are 1) a set of Linear Matrix Inequalities conditions that, by using the available data, allows the computation of the inner ellipsoidal approximation of the predecessor set of a given ellipsoidal set; and 2) a Direct Data-Driven Control scheme - named Robust Ellipsoidal Set-Theoretic Data-Enabled Predictive Control - in charge of controlling the plant by means of the previously computed sets. The resulting control strategy guarantees satisfaction of state and input constraints. Moreover, (a) in absence of process noise, the proposed solution is proven to coincide with the Model-based one; whereas (b) in the presence of noise, the proposed solution computes an inner approximation of the controllability sets able to maintain the required guarantees. Numerical simulations show that the proposed control scheme solves the problem of the constrained regulation to an equilibrium of an unknown plant by means of an informative trajectory without identification of the underlined dynamics, despite any realization of the process noise.
Keywords:
Trajectory
Noise
Computational modeling
Mathematical models
Data models
Symbols
Linear systems
Vectors
Time measurement
Data-driven control
ellipsoidal methods
predictive control
predictive control
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