Human-like control for offshore excavators

Human-like control for offshore excavators

Stijnman, Marco (TU Delft Mechanical, Maritime and Materials Engineering)

Abbink, D.A. (mentor)
van Wingerden, J.W. (mentor)
Kuiper, R.J. (graduation committee)
Mulders, S.P. (graduation committee)

Delft University of Technology

Mechanical Engineering | BioMechanical Design

2017-09-12

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.

human-like control
adaptive model predictive control
offshore excavator
extended kalman filter

http://resolver.tudelft.nl/uuid:f6e8b950-5b20-496e-8d23-a1a4af6ff922

Student theses

master thesis

© 2017 Marco Stijnman