Fast System Level Synthesis: Robust Model Predictive Control using Riccati Recursions

Autor:	Leeman, Antoine P., Köhler, Johannes, Messerer, Florian, Lahr, Amon, Diehl, Moritz, Zeilinger, Melanie N.
Rok vydání:	2024
Předmět:	Mathematics - Optimization and Control Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	System level synthesis enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algorithm for solving the corresponding disturbance feedback optimization problem for linear time-varying systems. The proposed algorithm iterates between optimizing the controller and the nominal trajectory while converging q-linearly to an optimal solution. We show that the controller optimization can be solved through Riccati recursions leading to a horizon-length, state, and input scalability of $\mathcal{O}(N^2 ( n_x^3 +n_u^3))$ for each iterate. On a numerical example, the proposed algorithm exhibits computational speedups by a factor of up to $10^3$ compared to general-purpose commercial solvers. Comment: Young Author Award (finalist): IFAC Conference on Nonlinear Model Predictive Control (NMPC) 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.13762 Zobrazit plný text záznamu View this record from Arxiv