Distributed MPC with ALADIN -- A Tutorial

Boris Houska; Jiahe Shi

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arXiv · 2204.01654

Distributed MPC with ALADIN -- A Tutorial

Boris Houska·Jiahe Shi

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Abstract

This paper consists of a tutorial on the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN) and its application to distributed model predictive control (MPC). The focus is - for simplicity of presentation - on convex quadratic programming (QP) formulations of MPC. It is explained how ALADIN can be used to synthesize sparse QP solvers for large-scale linear-quadratic optimal control by combining ideas from augmented Lagrangian methods, sequential quadratic programming, as well as barrier or interior point methods. The highlight of this tutorial is a real-time ALADIN variant that can be implemented with a few lines of code yet arriving at a sparse QP solver that can compete with mature open-source and commercial QP solvers in terms of both run-time as well as numerical accuracy. It is discussed why this observation could have far reaching consequences on the future of algorithm and software development in the field of large-scale optimization and MPC.

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BibTeX RIS

Boris Houska, Jiahe Shi. 2022-04-04. Distributed MPC with ALADIN -- A Tutorial. https://arxiv.org/abs/2204.01654

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