A Parallelizable Interior Point Method for Two-Stage Robust MPC
Artikel i vetenskaplig tidskrift, 2017

This paper presents a parallelizable algorithm for deploying a primal-dual interior point method on two-stage model predictive control (MPC) problems. The proposed method exploits the specific structure of the problem in order to achieve a parallelizable linear algebra. The focus is set on minimizing the amount of matrix factorizations performed in order to obtain a method of low computational complexity. For commonly used benchmark problems, considering robustness against 2-50 state space models, we show that if the overhead of the parallelization is negligible, the proposed method has a computational complexity per iteration only 5%-15% higher than the state-of-the art methods for standard MPC, provided that sufficiently many CPUs are available.

Interior point methods

parallel computations

robust model predictive control (MPC)


Emil Klintberg

Chalmers, Signaler och system, System- och reglerteknik

Sébastien Gros

Chalmers, Signaler och system, System- och reglerteknik

IEEE Transactions on Control Systems Technology

1063-6536 (ISSN)

Vol. 25 6 2087-2097 7802612


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