In order to make offshore working conditions safer, Ampelmann B.V. builds platforms that stay stable by compensating for a ship’s motion. These platforms are controlled via motion reference units (MRUs). In order to test the performances of these MRUs, a linear motion MRU test setup was developed. The system was divided into three parts: hardware, software and MRU assessment. This thesis focuses on the design and implementation of the MRU assessment part and on testing MRUs with the complete prototype setup. Ampelmann supplied the MRU with which the test were conducted. Validation tests have been done with a simulator and with the setup itself. Included in the assessment of the MRUs was the latency, signal-tonoise ratio and low-frequency behaviour, compared to a ground truth. Concluded from the tests was that the designed test setup has potential to accurately assess the performance of MRUs. There are some minor improvements to be made, but the overall system works as intended.

Formation control problems consider a set of mobile agents with the underlying goal of attaining and maintaining a state where the relative positions of agents are stable in accordance with the desired configuration. Navigation for formation control is typically achieved through localization in a global reference frame, e.g., via GNSS. However, when a global reference frame is not shared among agents, a relative navigation approach is required. Distributed filtering for relative localization in formation control systems is a relatively unexplored field. The absence of absolute positioning means motivates the need for a distributed filter that operates on the edges of the sensing graph of the multi-agent system. In this thesis, a data model for relative formation control problems and two edge-based Kalman filters are proposed. The first filter is designed for an individual edge. The second is a filter designed via decoupling of the optimal global filter which allows for the joint estimation of adjacent edges. It is shown that the joint filter is optimal under the decoupling constraints. Monte Carlo results show that when random environmental disturbances are correlated among agents, the joint filter outperforms the local edge filter in a mean square error sense. Lastly, systems are considered where inter-agent communications are unavailable, leading to biased prediction steps of the Kalman filters. We aim to minimize this effect through the proposal of a local Wiener filter which predicts the control actions of neighboring agents.

Formation control (FC) of multi-agent systems plays a critical role in a wide variety of fields. In the absence of absolute positioning, agents in FC systems rely on relative position measurements with respect to their neighbors. In distributed filter design literature, relative observation models are comparatively unexplored, and in FC literature, uncertainty models are rarely considered. In this article, we aim to bridge the gap between these domains, by exploring distributed filters tailored for relative FC of swarms. We propose statistically robust data models for tracking relative positions of agents in a FC network, and subsequently propose optimal Kalman filters for both centralized and distributed scenarios. Our simulations highlight the benefits of these estimators, and we identify future research directions based on our proposed framework.@en