System Identification and State Estimation for ROV uDrone

Abstract

This master thesis makes an introduction to all aspects of the design of an advanced model-based control system for the Remotely Operated Vehicle (ROV) uDrone. The design of thrust allocation along with a known mathematical model and the estimation of its parameters will be the basis for an implementation of a state estimation algorithm. The contribution can also be used in control systems implemented in the future on ROV uDrone. Further, the thesis discusses in detail the design of the worldwide known estimator called Kalman Filter along with a Linear-Quadratic Controller used in heave and yaw motion. Together they are known as LQG-control which is the optimal control for a given model together with the assumption that all noises are Gaussian distributed. Implementation in Simulink and interfaced with ROS proves uDrone as an excellent test bed. The results of the simulation and experimental results validate the design of thrust allocation, mathematical model, and estimator.