Robust Model Predictive Control for Linear Systems with State and Input Dependent Uncertainties

Danylo Malyuta; Behcet Acikmese; Martin Cacan

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arXiv · 1902.10984

Robust Model Predictive Control for Linear Systems with State and Input Dependent Uncertainties

Danylo Malyuta·Behcet Acikmese·Martin Cacan

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Abstract

This paper presents a computationally efficient robust model predictive control law for discrete linear time invariant systems subject to additive disturbances that may depend on the state and/or input norms. Despite the dependency being non-convex, we are able to capture it exactly for input dependency and approximately for state dependency in at most a second order cone programming problem. The formulation has linear complexity in the planning horizon length. The approach is thus amenable to efficient real-time implementation with a guarantee on recursive feasibility and global optimality. Robust position control of a satellite is considered as an illustrative example.

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BibTeX RIS

Danylo Malyuta, Behcet Acikmese, Martin Cacan. 2019-02-28. Robust Model Predictive Control for Linear Systems with State and Input Dependent Uncertainties. https://arxiv.org/abs/1902.10984

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