Unmanned aerial vehicles landing sequencing modelling using fuzzy logic

The basic method for sequencing and planning of arriving aircraft on a single runway is utilizing the First-Come-First-Served (FCFS) approach which allocates scheduled arriving time to each aircraft based upon the sequence implied by the earliest time that the aircraft can land. But unfortunately, it has been found that FCFS is rarely the best sequencing order in terms of maximum mission efficiency, minimum fuel consumption and delay reduction. Basically for sequencing and separating the aerial vehicles there are some inputs like speed, altitude, distance, rate of descend, endurance etc. On the other hand universal rules for sequencing and separating cannot be omitted. But finally we have only one output: "Who will be the number one for landing?" In that point, we propose a new model via fuzzy logic. The determination of conflicts between aircraft can be regarded as a very complex problem, yet air traffic controllers have the capacity to perform the undertaking with high rates of accomplishment under demanding circumstances. Much of the expertise of the air traffic controller appears to lie in the capacity to choose the suitable methodology for the issue. Fuzzy logic is a superset of ordinary (Boolean) logic that has been reached out to handle the idea of partial truth - truth values between "completely true" and "completely false". The importance of fuzzy logic derives from the way that most modes of human thinking and particularly practical judgment skills thinking are inexact in nature. In this paper, we present an analytic approach for UAV landing sequencing modelling with in the dynamic airspace including different mission types or applications for both military and civilian vehicles. For modelling, we utilize the MATLAB Fuzzy FIS (Fuzzy Inference System) with realistic data and create the user friendly interface with MATLAB/GUI. The simulation results show that the proposed model is a robust alternative for maximum mission efficiency, minimum fuel consumption and delay reduction.