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

TL;DR

This paper introduces an adaptive sampling methodology for dynamic systems to efficiently generate datasets for surrogate models, enabling fast and accurate approximations of complex models in real-time control applications.

Contribution

A novel adaptive sampling algorithm for dynamic models that maximizes feasible region coverage for improved surrogate model accuracy.

Findings

Effective in covering large feasible regions

Improves surrogate model accuracy and speed

Validated on chlor-alkali electrolysis model

Abstract

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.