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arXiv · 2103.01087

Stochastic Model Predictive Control for tracking of distributed linear systems with additive uncertainty

Christoph Mark·Steven Liu
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Abstract

In this paper, we propose a chance constrained stochastic model predictive control scheme for reference tracking of distributed linear time-invariant systems with additive stochastic uncertainty. The chance constraints are reformulated analytically based on mean-variance information, where we design suitable Probabilistic Reachable Sets for constraint tightening. Furthermore, the chance constraints are proven to be satisfied in closed-loop operation. The design of an invariant set for tracking complements the controller and ensures convergence to arbitrary admissible reference points, while a conditional initialization scheme provides the fundamental property of recursive feasibility. The paper closes with a numerical example, highlighting the convergence to changing output references and empirical constraint satisfaction.

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Christoph Mark, Steven Liu. 2023-03-06. Stochastic Model Predictive Control for tracking of distributed linear systems with additive uncertainty. https://doi.org/10.23919/ecc54610.2021.9655057

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