Adaptive Sampling of Dynamic Systems for Generation of Fast and Accurate Surrogate Models

Autor:	Talis, Torben, Weigert, Joris, Esche, Erik, Repke, Jens-Uwe
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavior surrounding a steady-state operation point. In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate models. The goal of the developed algorithm is to cover an as large as possible area of the feasible region of the original model. To demonstrate the performance of the presented framework it is applied on a dynamic model of a chlor-alkali electrolysis. Comment: 13 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.13826 Zobrazit plný text záznamu View this record from Arxiv