Learning of Intelligent Controllers for Autonomous Unmanned Combat Aerial Vehicles by Genetic Cascading Fuzzy Methods

Abstract Looking forward to an autonomous Unmanned Combat Aerial Vehicle (UCAV) for future applications, it becomes apparent that on-board intelligent controllers will be necessary for these advanced systems. LETHA (Learning Enhanced Tactical Handling Algorithm) was created to develop intelligent managers for these advanced unmanned craft through the novel means of a genetic cascading fuzzy system. In this approach, a genetic algorithm creates rule bases and optimizes membership functions for multiple fuzzy logic systems, whose inputs and outputs feed into one another alongside crisp data.A simulation space referred to as HADES (Hoplological Autonomous Defend and Engage Simulation) was created in which LETHA can train the UCAVs intelligent controllers. Equipped with advanced sensors, a limited supply of Self-Defense Missiles (SDM), and a recharging Laser Weapon System (LWS), these UCAVs can navigate a pre-defined route through the mission space, counter enemy threats, and destroy mission-critical targets. Multiple missions were developed in HADES and a squadron of four UCAVs was trained by LETHA. Monte Carlo simulations of the resulting controllers were tested in mission scenarios that are distinct from the training scenarios to determine the training effectiveness in new environments and the presence of deep learning.Despite an incredibly large sample space, LETHA has demonstrated remarkable effectiveness in training intelligent controllers for the UCAV squadron and shown robustness to drastically changing states, uncertainty, and limited information while maintaining extreme levels of computational efficiency. Her specific architecture is applicable to a wide array of topics and specializes in problems with limited distributed resources in a spatiotemporal environment containing uncertainties and unknowns.