Stochastic Model Predictive Control using Initial State and Variance Interpolation

Henning Schlüter; Frank Allgöwer

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arXiv · 2304.07122

Stochastic Model Predictive Control using Initial State and Variance Interpolation

Henning Schlüter·Frank Allgöwer

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Abstract

We present a Stochastic Model Predictive Control (SMPC) framework for linear systems subject to Gaussian disturbances. In order to avoid feasibility issues, we employ a recent initialization strategy, optimizing over an interpolation of the initial state between the current measurement and previous prediction. By also considering the variance in the interpolation, we can employ variable-size tubes, to ensure constraint satisfaction in closed-loop. We show that this novel method improves control performance and enables following the constraint closer, then previous methods. Using a DC-DC converter as numerical example we illustrated the improvement over previous methods.

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BibTeX RIS

Henning Schlüter, Frank Allgöwer. 2023-04-14. Stochastic Model Predictive Control using Initial State and Variance Interpolation. https://arxiv.org/abs/2304.07122

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