Dr. Alessandro Gardi is an Assistant Professor in the Department of Aerospace Engineering, focussing on aerospace cyber-physical systems (UAS, satellites, ATM systems and avionics). In this domain, he specialises in multi-objective trajectory optimization with emphasis on optimal control methods and AI/metaheuristics for air and space platforms. Dr Gardi has also worked on advancing systems and software engineering methodologies to new generations of aerospace and defence systems which exploit Internet of Things (IoT) technology and sophisticate AI/machine learning, health monitoring and data integrity/cyber-resilience functionalities to operate autonomously for extended periods of time even in degraded conditions.
To date, Dr Gardi has supervised more than 38 undergraduate and 16 postgraduate final year projects and several PhD/MRes candidates (7 completions). He has also been a partner/senior investigator in more than 10 research projects funded by industry and government, and has produced more than 200 refereed scientific publications, securing more than 2000 citations and an overall h-index of 28.
Dr Gardi holds an Honorary Associate position at RMIT University (Australia), where he also advises a number of postgraduate and postdoctoral researchers.
• 2022-current, Assistant Professor, Department of Aerospace Engineering, Khalifa University, UAE.
• 2019-2021, Senior Research and Teaching Fellow, RMIT University, Australia.
• 2017–2019, THALES Research and Teaching Fellow, RMIT University, Australia.
Novel Hybrid Methods for Multi-Objective Optimization of Aerospace Vehicle Trajectories
This project aims to develop new computationally-efficient hybrid algorithms to determine globally optimal solutions for a wide variety of aerospace trajectory optimization problems formulated as multi-objective and multi-phase optimal control problems. These hybrid algorithms fill the gap in the current state-of-the-art space trajectory optimization methods, which include highly accurate and computationally efficient pseudospectral methods on one side and globally optimal metaheuristic methods on the other side. The algorithms are applied to a set of contemporary aerospace vehicles in representative mission scenarios around the United Arab Emirates (UAE). The primary target for implementation of the proposed hybrid method is a Multidisciplinary Design Optimization (MDO) framework in a closed-loop design architecture. 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.
Project partners: RMIT University, Australian DoD/DST Group and SmartSat CRC
Intelligent Health and Mission Management for Trusted Autonomous Aerospace Vehicles
Capitalizing on the findings from recent PhD projects and industry/government collaborations in Australia on this topic, this project aims to develop Intelligent Health and Mission Management (IHMM) systems, which exploit Digital Twin, Artificial Intelligence (AI) and suitable onboard sensor networks to ensure operational safety in increasingly autonomous aerospace vehicles. The developed IHMM systems shall 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 aerospace 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 eVTOL and satellites. The developed IHMM system prototype will be experimentally verified in laboratory and ground/flight tests exploiting the available university facilities.
Project partners: RMIT University, SmartSat CRC, Australian DoD/DST Group, PHM Technology Pty Ltd, Insitech Pty Ltd
Development of Remote Sensing Systems for Smart Agriculture from UAS and Satellites
Considerable volumes of agricultural produce fail to meet the market’s quality expectations and is sold for a lower price or rejected. In order to meet market preferences and overcome supply chain challenges, the agricultural sector need to produce goods that satisfy consumer preference for size, taste, colour and texture; meet export market access protocols for air and sea freight; and that can survive periods of sub-optimal storage. Smart agriculture technology is therefore needed to track and predict diseases, produce quality attributes and harvest timing. This project aims to demonstrate that innovative remote sensing systems can be integrated on UAS and satellite platforms to enable product quality prediction and harvest optimization.
Project partners: RMIT University, FoodAgility CRC and SmartSat CRC
Currently accepting postgraduate students on the following MSc/BSc projects:
- Hybrid Methods for Multi-Objective Optimization of Aerospace Vehicle Trajectories
- Intelligent Health and Mission Management for Trusted Autonomous Aerospace Vehicles
- Physics-Based AI Modelling and Mitigation of Contrails Climate Impacts
- Safety-Critical Avionics Systems for Unsegregated Air and Space Transport Vehicle Operations (led by Prof. Roberto Sabatini)
- Artificial Intelligence for the Design and Operation of Distributed Space Systems (led by Prof. Roberto Sabatini)