Nonlinear Model Learning for Compensation and Feedforward Control of Real-World Hydraulic Actuators Using Gaussian Processes

Abdolreza Taheri*, Pelle Gustafsson, Marcus Rosth, Reza Ghabcheloo, Joni Pajarinen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

This paper presents a robust machine learning framework for modeling and control of hydraulic actuators. We identify several important challenges concerning learning accurate models of the dynamics for real machines, including noise and uncertainty in state measurements, nonlinear effects, input delays, and data-efficiency. In particular, we propose a dual-Gaussian process (GP) model architecture to learn a surrogate dynamics model of the actuator, and showcase the accuracy of predictions against the piecewise and neural network models that have been widely used in the literature. In addition, we provide robust techniques for learning neural network inverse models and controllers by batch GP inference in an automated, seamless and computationally fast manner. Finally, we demonstrate the performance of the trained controllers in real-world feedforward and tracking control applications.

Original languageEnglish
Pages (from-to)9525-9532
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number4
DOIs
Publication statusPublished - 1 Oct 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Hydraulic actuators
  • Machine learning for robot control
  • Model learning for control

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