Papers › Fast System Level Synthesis: Robust Model Predictive Control using Riccati Recursions
Fast System Level Synthesis: Robust Model Predictive Control using Riccati Recursions
Antoine P. Leeman, Johannes Köhler, Florian Messerer, Amon Lahr, Moritz Diehl, Melanie N. Zeilinger
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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 𝒪(N² ( nₓ³ +nᵤ³)) for each iterate. On a numerical example, the proposed algorithm exhibits computational speedups by a factor of up to 10³ compared to general-purpose commercial solvers.
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