The development of an Active Reflection Compensation (ARC) algorithm for a wave generator is challenging. Water behaves in a strong nonlinear way and the time available for the computation of the compensating segment displacements is short. Especially for the case where the wave height sensors are mounted on the surface of the segments. The current ARC algorithm of Bosch Rexroth is based on an approximation of the Biésel transfer function. Although this method gives fair results, it lacks performance regarding the absorption of oblique waves. This report introduces a wave model identification method based on the Discrete Fourier Transform (DFT) and Extended Kalman Filter (EKF). With the EKF having the ability to include measurement noise and determine model uncertainty, this leads to reasonable estimates of complex second order waves. This report does not only cover the identification part of the algorithm but the full ARC algorithm from wave height measurements to a time series for the segment displacements. Although the results are based on simulations, the algorithm shows a much improved absorption performance for a wide range of propagation directions. It shows decent results for tests with several different sets of wave characteristics. The identification process needs time to mature, which leads to a decrease in absorption performance in the early stage of the free-surface elevation time series. A hybrid method is designed, in which the short-term absorption is based on the current algorithm and, after a certain time interval, the model based algorithm will take over.

Objects floating on or near the water surface (e.g. vessels, and floating wind-turbines) suffer from motions induced by waves of varying height, direction, and frequency. This not only causes unpleasantness for passengers and crew of ships but also it limits the accessibility to the offshore platforms. Bosch Rexroth with their partner Barge Master have developed the so-called Motion Compensated Gangway that provides a safe passage for cargo and personnel to offshore structures.
In order to maintain a motionless connection with the offshore structure, once the tip of the gangway is pushed against the offshore structure. The gangway system actively compensates for the sea-induced motion that acts on the vessel.
However, the docking procedure is still manually attained, where accidents may occur due to human error (i.e. insufficient training, loss of concentration). One way to improve the current control scheme is to enable an automated docking scheme.
Accordingly, the main of this project focuses on eliminating the human factor from the control loop, so the overall process is accomplished automatically and more efficiently in terms of safety and performance.
Inspired by how the operator estimates the relative motion between the Gangway and the target (i.e. the offshore platform). In this thesis, a measurement system is proposed to measure this relative motion. This measurement system comprises a vision sensor, force tip measurements, and Motion Reference Unit (MRU). In this thesis, the proposed automated docking scheme is developed around a nonlinear MPC scheme. For the simulation environment and for the MPC scheme employs, a nonlinear model of the gangway system is derived. This model embeds an approximation of the joint-level control loop of the Gangway system. Also, this model comprises the open-chain kinematic model of the Gangway system and the proposed measurement system including a perspective projection model of the vision sensor. Due to modelling the vision sensor as such and the MPC’s capability in handling various constraints, the proposed control scheme enjoys a singularity-free solution.
The proposed control scheme detects and tracks the target in the 2D image plane. To safeguard against visual measurements discontinuity (i.e. cluttering, target outside the field of view), a linear Kalman filter is designed to predict the target position in the image plane.
To gain higher performance, the disturbance anticipatory property in MPC is enabled by forecasting the sea-induced motion. Where a neural network with the NARX topology was designed and trained to acquire a multi-step-ahead prediction model of the induced motion.
Several numerical experiments were carried to evaluate the performance of the proposed control scheme for automated docking. Where for the nominal case scenarios all the control requirements are fulfilled. Also, more extreme scenarios are performed to evaluates the overall performance under plant model mismatch and against various sea-induced motion conditions. Evidently, the proposed control scheme is prone to camera calibrations error.
In terms of the efficiency, the proposed automated docking scheme performs the docking in 4 to 10 seconds (based on the initial conditions and sea state). Whereas the time it takes the operator to perform the docking is up to 3 minutes which depends on his/her experience.