Robustifying Model Predictive Control of Uncertain Linear Systems with Chance Constraints

This paper proposes a model predictive controller for discrete-time linear systems with additive, possibly unbounded, stochastic disturbances and subject to chance constraints. By computing a polytopic probabilistic positively invariant set for constraint tightening with the help of the computation...

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Bibliographic Details
Published inProceedings of the IEEE Conference on Decision & Control pp. 3650 - 3655
Main Authors Wang, Kai, Hoang, Kiet Tuan, Gros, Sebastien
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2024
Subjects
Computational modeling
Linear systems
Predictive control
Predictive models
Probabilistic logic
Quadratic programming
Robustness
Stability analysis
Stochastic processes
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ISSN2576-2370
DOI10.1109/CDC56724.2024.10886384

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Summary:This paper proposes a model predictive controller for discrete-time linear systems with additive, possibly unbounded, stochastic disturbances and subject to chance constraints. By computing a polytopic probabilistic positively invariant set for constraint tightening with the help of the computation of the minimal robust positively invariant set, the chance constraints are guaranteed, assuming only the mean and covariance of the disturbance distribution are given. The resulting online optimization problem is a standard strictly quadratic programming, just like in conventional model predictive control with recursive feasibility and stability guarantees and is simple to implement. A numerical example is provided to illustrate the proposed method.
ISSN:2576-2370
DOI:10.1109/CDC56724.2024.10886384