Estimating Unknown Parameters in Mechatronic Systems Using Data-Driven Surrogates

EasyChair Preprint 13493

Abstract

This study focusses on using an AI-based, data-driven surrogate model to estimate unknown parameters in mechatronic systems, focusing on the payload estimation at the end of a hydraulically actuated flexible boom. A feedforward neural network (FFN) was developed and trained using data from a commercial multibody software to predict unknown masses with high accuracy (98.5%). The study achieved promising results, with the FFN effectively predicting payload not included in the training set with minimal error. The findings suggest future research directions, including the estimation of variable payload and the use of automated hyperparameter tuning and comparing with extended Kalman filters, to enhance the AI-based control and maintenance of complex systems. Further investigations are needed to compare these AI methods with traditional model-based controllers.

Keyphrases: Data-driven surrogates, Multibody, feedforward neural network, hydraulics, machine learning

@booklet{EasyChair:13493,
  author    = {Anton Kostiainen and Qasim Khadim and Emil Kurvinen},
  title     = {Estimating Unknown Parameters in Mechatronic Systems Using Data-Driven Surrogates},
  howpublished = {EasyChair Preprint 13493},
  year      = {EasyChair, 2024}}