Anytime algorithms for estimation-based model predictive control

基于估计的模型预测控制的随时算法

• 批准号: 399211811
• 负责人: Professor Dr.-Ing. Christian Ebenbauer
• 金额: --
• 依托单位: Lehrstuhl für Intelligente Regelungssysteme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/399211811?language=en
• 关键词: Anytime; algorithms; estimation; based; model

In the course of the continuously advancing digitization, the systematic and efficient analysis of a big amount of measurement data, particularly by means of optimization-based estimation procedures, is one of the big future challenges in the field of automation and control of technical processes. In particular, this applies to modern model-based predictive control approaches, which in many cases require to estimate the current system state and/or other process parameters from measurements in order to predict and optimize the system behavior. However, despite the enormous conceptual and practical relevance, there exist up to now no approaches that allow for a systems theoretically sound and numerically efficient integration of dynamical on-line measurements into model-based predictive control schemes. Motivation and basic idea of this research project is therefore the design and development of a novel class of estimation-based model predictive control procedures, which combine the estimation of relevant state information and process parameters in an integrative and optimization-based framework with a predictive control scheme. In particular, the overall goal is an innovative and systems theoretically sound methodology for the design of so-called anytime algorithms, which allow to ensure important stability and real-time guarantees of estimation and control even in cases where the underlying optimization algorithms perform only a limited number of iterations, for example due to big amounts of measurements or limited computational resources.

在不断推进的数字化过程中，对大量测量数据的系统和有效分析，特别是通过基于优化的估计过程，是技术过程自动化和控制领域未来的重大挑战之一。特别地，这适用于现代的基于模型的预测控制方法，其在许多情况下需要从测量估计当前系统状态和/或其他过程参数，以便预测和优化系统行为。然而，尽管有巨大的概念和实际意义，到目前为止，还没有方法，允许系统理论上健全和数字有效的动态在线测量集成到基于模型的预测控制方案。因此，本研究项目的动机和基本思想是设计和开发一类新的基于估计的模型预测控制程序，该程序将相关状态信息和过程参数的估计结合在一个集成和基于优化的框架中，并具有预测控制方案。特别地，总体目标是用于设计所谓的随时算法的创新的和系统理论上合理的方法，其允许确保估计和控制的重要稳定性和实时保证，即使在底层优化算法仅执行有限次数的迭代的情况下，例如由于大量的测量或有限的计算资源。