Learning Adaptive Controller for Hydraulic Machinery Automation
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Date
2024-04
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
The automation of hydraulic machinery has the potential to improve productivity and reduce human labor in many industries. However, the complex dynamics of hydraulic actuators, variability from machine to machine, and system degradation over time make it challenging to design controllers for hydraulic machine automation. Consequently, existing approaches rely on manual tuning and data collection. In this letter, we propose an approach to train an adaptive controller for this problem. The controller can be trained purely in simulation, and at the time of deployment, it can adapt to the dynamics of the real system within minutes. After the adaptation, precise motion control can be achieved. We validated the approach by testing a single controller trained with the proposed method on two hydraulic machines that are distinctly different in size, application, and age. The results show comparable control performance of our general approach compared to previous methods, which rely on machine-specific data and training.
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Publication status
published
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Book title
Journal / series
Volume
9 (4)
Pages / Article No.
3972 - 3979
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Robotics and automation in construction; hydraulic/pneumatic actuators; reinforcement learning
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication