A Parallelizable Interior Point Method for Two-Stage Robust MPC

作者：Klintberg Emil^*; Gros Sebastien

来源：IEEE Transactions on Control Systems Technology, 2017, 25(6): 2087-2097.

DOI：10.1109/TCST.2016.2638680

• Interior point methods
• parallel computations
• robust model predictive control (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.

• 出版日期2017-11