Robust Stability Analysis of a Simple Data-Driven Model Predictive Control Approach
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Date
2023-05
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Journal Article
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Abstract
In this article, we provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme. The formulation does not involve any terminal ingredients, thus allowing for a simple implementation without (potential) feasibility issues. The proposed approach relies on an implicit description of linear time-invariant systems based on behavioral systems theory, which only requires one input-output trajectory of an unknown system. For the nominal case with noise-free data, we prove that the data-driven MPC scheme ensures exponential stability for the closed loop if the prediction horizon is sufficiently long. Moreover, we analyze the robust data-driven MPC scheme for noisy output measurements for which we prove closed-loop practical exponential stability. The advantages of the presented approach are illustrated with a numerical example.
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published
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68 (5)
Pages / Article No.
2625 - 2637
Publisher
IEEE
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Software
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Subject
Data-driven control; predictive control for linear systems; uncertain systems; optimal control