Modern Navy ship operations are undergoing significant transformations, marked by three key changes. Firstly, ships become increasingly complex, characterized by advanced systems generating vast amounts of sensor data. Secondly, crew sizes become smaller, placing greater responsibility on individuals to oversee a multitude of systems. Lastly, as warfare becomes faster, naval forces are compelled to make quicker decisions in response to rapidly changing and complex situations. Consequently, there is a pressing demand for integrating information from diverse sources and providing clearer, comprehensive overviews to facilitate decision-making. In the event of a calamity, it is crucial to rapidly assess the impact of the calamity on the ship's capabilities. This study introduces an innovative approach for enhancing situation awareness of the situation inside navy ships. By employing 3D geo-information and a knowledge graph we evaluate the consequences of calamities on the operational capabilities of the ship. We construct a detailed 3D model of both exterior and interior parts of a ship using three dimensional geometric data, which includes static elements such as rooms, (weapons) systems, and cable & pipe networks. This model also contains logical data describing functionality and interconnectivity of the physical objects in the shup. To do this, the data elements are connected through a linked data approach. In this research project the 3D model will be used in two key ways: firstly, to generate a realtime 3D common operational picture of the “internal battle” that visually represents the calamities and their impacts in 2D, 2.5D and real3D, all representing the same situation; secondly, as a 3D computational model equipped with a comprehensive set of business relating events in the ship. This model enables realtime assessment of calamity impacts using data gathered from sensors, personnel, and systems throughout the ship, to be used in a 3D decision support system. Data from smoke and temperature sensors are integrated with the static 3D model, as are simulated positions of personnel. Users can choose their own views (depending on their roles) but can also continue with the already existing views of other users. The conclusion is that a knowledge graph in linked data and a 3D representation of the same ship in realtime will give a basis for better decision-making. The effectiveness of this approach is tested with personnel from the Royal Netherlands Navy with data and models of one navy ship. The current state of the project is that the spatial and semantic relationships, the different visualizations, and interfaces have been developed. This first paper will report on the design of all this, the scientific approach and the first results of user testing.@en

Control augmentation systems based on Incremental Nonlinear Dynamic Inversion (INDI) are able to provide high-performance nonlinear control without a holistic model. Considering an angular rate control law for a fixed-wing aircraft, only a control effectiveness (CE) model and angular acceleration measurement feedback is required. Despite enhanced robustness against parametric model mismatches due to reduced model dependency, the performance of INDI-based control laws can still vary due to inaccurate CE models. This paper confirms that longitudinal centre of gravity (CG) shifts and CE uncertainty result in varying handling qualities and stability (HQ\&S) characteristics. An adaptive solution using Least-Mean-Square (LMS) based parameter estimation is investigated to address these variations. The results demonstrate that online CE model correction result in reduced HQ\&S variation. However, it was found that some flight conditions together with adverse CG shifts could lead to violation of the time-scale separation assumption that underlies the adaptive control law design. As this assumption is inherent to the INDI control design itself as well, online CE model correction is only partly able to resolve the resulting performance variations.@en

The recently introduced twisted and coiled polymer muscle is an inexpensive and lightweight compliant actuator. Incorporation of themuscle in applications that rely on feedback creates the need for deflection and force sensing. In this paper, we explore a sensing principle that does not require any bulky or expensive additional hardware: Self-sensing via electrical impedance. To this end, we characterize the relation between electrical impedance on the one hand, and deflection, force, and temperature on the other hand for the Joule-heated version of this muscle. Investigation of the theoretical relations provides potential fit functions that are verified experimentally. Using these fit functions results in an average estimation error of 0.8%, 7.6%, and 0.5%, respectively, for estimating deflection, force, and temperature. This indicates the suitability of this self-sensing principle in the Joule-heated twisted and coiled polymer muscle.

@en