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Hybrid Multi-Objective Optimization Methods for Aerospace GNC Systems

This project aims to develop new computationally-efficient algorithms to determine globally optimal solutions for various aerospace trajectory planning and vehicle/system design tasks formulated as multi-objective and multi-phase problems. The algorithms shall capitalize on the complementary advantages of highly accurate and computationally efficient gradient-based methods on one side and globally optimal metaheuristic methods on the other side. The project focusses on contemporary aerospace vehicles in representative mission scenarios around the United Arab Emirates (UAE). The primary target for implementation of the developed methods are the Guidance, Navigation and Control (GNC) systems of these vehicles. The implementation in a prototype 4D trajectory Planning and Negotiation/Validation (4-PNV) system for strategic and tactical online trajectory planning in shared airspace is also pursued, particularly tackling the challenges airising in UAS Traffic Management (UTM) and Urban Air Mobility (UAM) scenarios.

Project partners: RMIT University (Australia), University of Bologna

Wake and Atmospheric Boundary Layer Turbulence Modelling and Avoidance for Operational Safety in Urban Air Mobility

The anticipated growth of Uncrewed Aircraft Systems (UAS) and Urban Air Mobility (UAM) is introducing a variety of challenges which will have to be addressed to ensure operational safety of these emerging vehicles in and around densely built environments, particularly when the transportation of passengers is involved. The wake of other UAM vehicles and the gusts and turbulence occurring due to the flow interactions with buildings pose significant hazards, particularly for smaller vehicles with limited manoeuvrability. In order to design Guidance, Navigation and Control (GNC) and Sense-and-Avoid (SAA) systems capable of avoiding these hazards, it is essential to characterize these phenomena and develop suitable mathematical models.

This project is part of a multi-university collaboration and aims to:

1. conduct a series of wind tunnel experiments to fill the existing gaps in the characterization of wake turbulence of multirotor / Vertical Take-Off and Landing (VTOL) UAS and Atmospheric Boundary Layer (ABL) turbulence in densely built environments;
2. develp suitable theoretical and empirical models to predict these hazardous phenomena around various classes of vehicles and buildings;
3. develop and test new GNC and SAA system prototypes which integrate the developed models to achieve the safe avoidance of hazardous turbulence and gusts in the considered scenario.

Project PI: Dr Vladimir Parezanovic (KU Aerospace)
Project partners: University of Liverpool (UK)

Intelligent Health Management and Trusted Autonomy in Aerospace Vehicles

This project aims to develop Intelligent Health and Mission Management (IHMM) systems exploiting digital twin, explainable/certifiable Artificial Intelligence (AI) and suitable onboard sensor network technologies to ensure operational safety in increasingly autonomous aerospace vehicles such as Uncrewed Aircraft Systems (UAS) and intelligent satellite systems. In these vehicles, IHMM systems are expected to progressively replace the conventional flight crew in detecting the onset of anomalies and in planning and enacting reversionary actions, as without these critical IHMM capabilities an autonomous vehicle would be prone to catastrophic failures. The project will focus on the integrity of the most important safety-critical systems in contemporary aerospace vehicles, such as: UAS sense-and-avoid systems, electric power and propulsion systems and flight control systems for electric Vertical Take-Off and Landing (eVTOL) vehicles and intelligent satellite systems. The developed IHMM system prototype will be experimentally verified in laboratory and ground/flight tests exploiting the available university facilities.