A master thesis from Aalborg University
MPC control for the BlueROV2 - Theory and Implementation
Author(s)
Term
4. Term
Publication year
2020
Submitted on
2020-10-19
Pages
112 pages
Abstract
Model Predictive Controller (MPC) controllers are commonly used in control of process plants, but due to the high computation load, they have not been that popular for small embedded systems. Due to big leaps in the computational power of small embedded computers, MPCs can be implemented on a wide variety of other embedded control problems. In this thesis, we modify the software and hardware of the original commercially available open-source platform BlueROV2 to make it more suitable and better optimized to run an MPC and a Kalman filter. The code is written in Python using object-based programming principles. A comparison between the original system and our modifications is made. To implement an MPC controller on this modified system, the Remotely Operated Vehicle (ROV) is modelled in 6 Degrees of Freedom (DoF), and the model is validated. State feedback for the controller is done using a Kalman filter for sensor fusion of the Inertia Measurement Unit (IMU) and Underwater Global Positioning System (UGPS). Then, a comparison is made with a Proportional–Integral–Derivative (PID) and an LQR implemented on the BlueROV2.
Keywords
MPC ; ROV ; Kalman filter ; Sensor fusion ; Python
Documents
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