论文标题
与Aladin分布式MPC - 教程
Distributed MPC with ALADIN -- A Tutorial
论文作者
论文摘要
本文由有关增强Lagrangian的交替方向不可活力（Aladin）及其应用于分布式模型预测控制（MPC）的教程。重点是 - 为了简单，在MPC的凸二次编程（QP）公式上。可以通过结合增强拉格朗日方法，顺序二次编程以及障碍物或内部点方法的想法来解释如何使用Aladin来合成稀疏的QP求解器，以实现大规模线性季度最佳控制。本教程的亮点是一个实时的Aladin变体，可以使用几行代码来实现，但可以使用稀疏的QP求解器，可以根据运行时以及数值准确性与成熟的开源和商业QP求解器竞争。讨论了为什么这种观察可能会对大规模优化和MPC领域的算法和软件开发的未来产生巨大影响。
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.
