An Artificial Neural Networks (ANN) Based System for Optimal Taxiing Navigation of a BOEING-747 Aircraft

Congestion is considered to be one of the most problematic issues in international airspace. In the twenty-first century, European airports experience many challenges, including those relating to capacity and the environment. Airports and their transportation systems can avoid the occurrence of huge bottleneck problems via appropriate expansion plans or the efficient utilization of existing resources. The investment in infrastructure can help in expanding the airport surface area which leads to increase the capacity of the airports. However, this is expensive in most cases and can lead to harmful effects on the environment, which may lead to noise and pollution as well as increasing the overall complexity of operations which thus add to more workload. Airports work almost closer to their maximum capacity. The continued increase in airport surface area can be difficult and costly, so we argue here that research should focus on finding solutions that use the existing space in more efficient ways rather. Decision support systems, planning and scheduling have to be increasingly and continuously improved and advanced. Improving the efficiency of the airports is considered to be one of the most important issues of aircraft ground management, because it is a link to all other ground operations which involves the coordination of machines and humans. The attempt to taxi an aircraft in an optimal and efficient manner using automatic systems are increasingly being deployed across major airports across the world. However, many of these systems do not use adequate aircraft models, nor do they continuously seek to optimize objective functions such as minimizing fuel consumption and minimizing harmful greenhouse gas emissions. In this paper, a new approach is proposed for optimal taxiing navigation of a high-fidelity aircraft model. A routing and scheduling algorithm that determines the waypoints, taxi route and time deadline is conjunct to work with the new approach. The proposed approach, which integrated a MATLAB-Simulink model of the BOEING-747 aircraft with Artificial Intelligence (AI) base control successful generates fuel-efficient 4DTs in real time, while taking constraints on operations into account.