A Parallelizable Interior Point Method for Two-Stage Robust MPC

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.

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