Unmanned Aircraft Systems (UAS) have been increasing in popularity in personal, commercial, and military applications. The increase of the use of UAS poses a significant risk to general air travel, and will burden an already overburdened Air Traffic Control (ATC) network if the Air Traffic Management (ATM) system does not undergo a revolutionary change. Already there have been many near misses reported in the news with personal hobbyist UAS flying in controlled airspace near airports almost colliding with manned aircraft. The expected increase in the use of UAS over the upcoming years will exacerbate this problem, leading to a catastrophic incident involving substantial damage to property or loss of life. ATC professionals are already overwhelmed with the air traffic that exists today with only manned aircraft. With UAS expected to perform many tasks in the near future, the number of UAS will greatly outnumber the manned aircraft and overwhelm the ATC network in short order to the point where the current system will be rendered extremely dangerous, if not useless. This paper seeks to explore the possibility of using the artificial intelligence concept of fuzzy logic to automate the ATC system in order to handle the increased traffic due to UAS safely and efficiently. Automation would involve an algorithm to perform arbitration between aircraft based on signal input to ATC ground stations from aircraft, as well as signal output from the ATC ground stations to the aircraft. Fuzzy logic would be used to assign weights to the many different variables involved in ATM to find the best solution, which keeps aircraft on schedule while avoiding other aircraft, whether they are manned or unmanned. The fuzzy logic approach would find the weighted values for the available variables by running a simulation of air traffic patterns assigning different weights per simulation run, over many different runs of the simulation, until the best values are found that keep aircraft on schedule and maintain the required separation of aircraft.
International Journal of Electrical and Computer Engineering (IJECE), Vol. 8, No. 5, October 2018, pp. 3169-3178.