A Training-Free Data-Driven Method for Input-Output Modeling of Complex Processes

In a variety of human-in-the-loop systems, variations among human operators can result in inconsistencies in process operation and product quality. While a variety of methods exist to mitigate this issue, they often require some model of the relationship between the human input and system output; unfortunately, obtaining such a model continues to be very difficult for highly complex processes such as industrial manufacturing processes. In this paper, we propose an innovative training-free data-driven (TFDD) modeling method that directly predicts the next state from the state transition information of all samples in a database. Because the prediction is directly derived from the database, the model does not require any training, nor does the model architecture change from one application to another. Through a case study on human operator supervisory control of twin-roll steel strip casting, we demonstrate the performance and advantages of the proposed TFDD method as compared to a baseline nonlinear autoregressive network with exogenous inputs (NARX) model trained using the same dataset. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)