Enhanced Machine Learning for Real-Time Plant Replication in Embedded Systems
Year: 2024
Type: conference-paper
Abstract: This paper examines the use of supervised machine learning to construct a digital twin model replicating a physical plant. An inverted pendulum simulation has been used as a case study.A comparative study was conducted on single-step and multistep Dense models, Convolutional Neural Network (CNN), Recurrent Neural Net (RNN), and a Residual Neural Net (RNN2) models to investigate the most appropriate model for replicating the plant model.The study found that single-step models were consistently more accurate than multi-step models due to single-step model’s iterative nature. Whereas, multi-step models were better at revealing prediction patterns and identifying causes for large deviations. The best-performing model was the RNN2 model, however, signs of overfitting were observed. In general, all models were able to take into account minor random actuation, however, large changes such as the pendulum falling over caused the models to behave sporadically.
Authors: Abhisek Chowdhury, Jane Jung, Matthew M. Y. Kuo, Roopak Sinha
Institutions: Auckland University of Technology, Deakin University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408281081