Numerical methods for ellipsoid based and tree sparse robust MPC formulations

该项目提案是与RWTH亚琛、TU柏林和TU多特蒙德的研究小组一起提出的一揽子提案的一部分，涉及鲁棒模型预测控制（MPC）的有效数值方法。主要目的是扩展基于树和基于椭球传播的鲁棒MPC方法的最新数值方法，并在高效并行开源软件中实现和联合收割机结合这些方法。这项工作可以建立在研究小组开发的现有MPC方法和软件的基础上，将通过新的鲁棒公式算法进行扩展，该项目的一个重要组成部分是协调软件包提案和收集真实世界鲁棒MPC应用模型的精确数学公式，并在模拟中测试和评估这些模型的新数值方法。

This project proposal which is part of a package proposal together with research groups at RWTH Aachen, TU Berlin and TU Dortmund concerns efficient numerical methods for robust model predictive control (MPC).The principal aim is to extend the state-of-the-art in numerical methods for tree-based and ellipsoidal-propagation-based robust MPC methods, and to implement and combine these methods in efficient and parallel open-source software.The work can build on existing MPC methods and software developed in the research group, which shall be extended by new algorithms for robust formulations, which exploit the specific structures and possibilities for parallelization that these formulations entail.An important part of project is the coordination of the package proposal and the collection and exact mathematical formulation of models of real-world robust MPC applications and to test and evaluate the new numerical methods on those models in simulation.