Human-like control for offshore excavators
M.J. Stijnman (TU Delft - Mechanical Engineering)
David Abbink – Mentor
J.W. van Wingerden – Mentor
Roel Kuiper – Graduation committee member
Sebastian Paul Mulders – Graduation committee member
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Offshore excavators are large hydraulically driven machines which are difficult to control due to slow dynamics, inherent nonlinearities, a varying environment and complex kinematics. As digging is performed under water, only limited visual feedback of the task can be provided by means of a visualization interface. Operators require an extensive amount of practice before being capable of achieving sufficient and consistent performance. Often, automation is implemented as a means of reducing costs related to expensive operators and attaining consistent performance. However, automation struggles with adapting to unforeseen situations and a large task variety, which are areas human operators excel in. Instead of attempting to fully automate excavators, this thesis takes a more human-centered approach, and focuses on the design and evaluation of a human-like controller to partially automate excavator operations, while assuming a human operator is still present to trade or share control with. In order to simultaneously deal with the various nonlinearities in the system while providing human-like control this work proposes the use of an Adaptive Model Predictive Controller, whose underlying principles are similar to those of humans.To determine whether the controller is indeed human-like a complex excavator model including a realistic soil model was developed and used to implement and tune the controller. Finally, a simulator experiment was conducted to compare the subjects and the controller in terms of performance for various tasks and the control behavior similarity for a well-trained task. Eight subjects controlled the excavator model and performed four stages, starting with a familiarization stage in which the subject got accustomed to the system. The other three stages (easy, difficult, boulder) featured a 9 m long target path, with conditions of varying difficulty between stages. The controller showed 2 to 3 times lower tracking errors for both the easy and difficult stage while providing 1.5 to 5 times smoother inputs, but could not overcome the unforeseen boulder whereas all subjects could, showcasing the importance of having humans and automation complement each other. Furthermore, a high quality fit (VAF > 70%) was found between the boom inputs of the subjects and the controller in the well-trained easy stage, indicating human-like control.