PolyOCP.jl -- 一个用于随机优化预测控制和模型预测控制的Julia包
PolyOCP.jl -- A Julia Package for Stochastic OCPs and MPC
AI总结：
本文介绍PolyOCP.jl，一个用于解决线性系统中多种扰动分布的随机优化预测控制问题的Julia工具包，通过多项式混沌展开方法实现高效求解。
摘要：
考虑最优和预测控制中的随机不确定性是一个广泛研究的领域。最近，多项式混沌展开（PCE）因涉及随机不确定系统参数的问题以及具有加性随机i.i.d.扰动的问题而受到广泛关注。尽管存在多个开源PCE工具箱，但针对涉及加性随机i.i.d.扰动的优化预测控制（OCP）问题的专用开源代码在Julia中并不可用。因此，本文介绍了PolyOCP.jl工具包，该工具包能够高效地解决线性系统中大量扰动分布的随机OCP问题。我们解释了PCE转换随机OCP的主要数学概念以及这些概念如何在工具包中实现。我们通过两个示例来说明PolyOCP.jl的功能。
英文摘要：
The consideration of stochastic uncertainty in optimal and predictive control is a well-explored topic. Recently Polynomial Chaos Expansions (PCE) have received considerable attention for problems involving stochastically uncertain system parameters and also for problems with additive stochastic i.i.d. disturbances. While there exist a number of open-source PCE toolboxes, tailored open-source codes for the solution of OCPs involving additive stochastic i.i.d. disturbances in julia are not available. Hence, this paper introduces the toolbox PolyOCP$.$jl which enables to efficiently solve stochastic OCPs for linear systems subject to a large class of disturbance distributions. We explain the main mathematical concepts between the PCE transcription of stochastic OCPs and how they are provided in the toolbox. We draw upon two examples to illustrate the functionalities of PolyOCP$.$jl.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
