TY - JOUR
T1 - Nonlinear Model Learning for Compensation and Feedforward Control of Real-World Hydraulic Actuators Using Gaussian Processes
AU - Taheri, Abdolreza
AU - Gustafsson, Pelle
AU - Rosth, Marcus
AU - Ghabcheloo, Reza
AU - Pajarinen, Joni
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - 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.
AB - 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.
KW - Hydraulic actuators
KW - Machine learning for robot control
KW - Model learning for control
UR - http://www.scopus.com/inward/record.url?scp=85135457299&partnerID=8YFLogxK
U2 - 10.1109/LRA.2022.3190808
DO - 10.1109/LRA.2022.3190808
M3 - Article
AN - SCOPUS:85135457299
SN - 2377-3766
VL - 7
SP - 9525
EP - 9532
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 4
ER -