In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of small unmanned aerial systems (sUAS) continue to multiply. Therefore, finding solutions to managing sUAS in highly congested airspace will facilitate sUAS operations. In this study, a fuzzy logic-based approach was used to help mitigate the risk of collisions between aircraft using separation assurance and collision avoidance techniques. The system was evaluated for its effectiveness at mitigating the risk of mid-air collisions between aircraft. This system utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. The avoidance logic was verified using formal methods and shown to select the correct action in all instances. Additionally, the fuzzy logic controllers were shown to always turn the vehicles in the correct direction. Numerical testing demonstrated that the avoidance system was able to prevent a mid-air collision between two sUAS in all tested cases. Simulations were also performed in a three-dimensional environment with a heterogeneous fleet of sUAS performing a variety of realistic missions. Simulations showed that the system was 99.98% effective at preventing mid-air collisions when separation assurance was disabled (unmitigated case) and 100% effective when enabled (mitigated case).
Part of the book: Modern Fuzzy Control Systems and Its Applications
Fuzzy logic is used in a variety of applications due to its universal approximator attribute and non-linear characteristics. The tuning of the parameters of a fuzzy logic system, viz. the membership functions and the rulebase, requires a lot of trial and error. This process could be simplified by using a heuristic search algorithm like genetic algorithm (GA). In this chapter, we discuss the design of such a genetic fuzzy controller that can control an inverted double pendulum. GA improves the fuzzy logic controller (FLC) with each generation during the training process to obtain an FLC that can bring the pendulum to its inverted position. After training, the effectiveness of the FLC is tested for different scenarios by varying the initial conditions. We also show the effectiveness of the FLC even when subjected to noise and how the performance improves when the controller is tuned with noise.