Fuzzy Logic Based Intelligent Agents for Unmanned Combat Aerial Vehicle Control

Abstract

Volume 6 • Issue 1 • 1000139 J Def Manag ISSN: 2167-0374 JDFM, an open access journal The GFT methodlogy utilizes a series of Fuzzy Inference Systems (FISs) with varying degrees of connection between them. By breaking up the problem into many sub-decisions in a dynamic programming fashion, the solution space is significantly reduced. Unlike in Fuzzy Decision Trees or Fuzzy Networks, the nodes of a GFT are not individual components of FISs, but rather are unique FISs themselves [3-5]. Any coupling between inputs and outputs must be captured as best as possible. Thus, branches of the tree are utilized to capture inputs that are related, and connections between branches to certain FISs allow inputs to be considered in other FISs not directly within their same branch. As the number of if-then rules required for a fuzzy controller is exponential, based upon the number of membership functions of the inputs, this type of approach keeps the number of parameters as low as possible and allow a learning system to train the system. Figure 2 depicts a visualization of LETHA's GFT, with different branches governing routing, weapons, and communications. Of note in Figure 2 is the ease in which other methods and algorithms can be incorporated into the system in different levels. A Cooperative Task Assignment Algorithm, Fuzzy Clustering Route Solver, and No Communications Fire Control System are directly incorporated [6-8]. This system requires a strong learning system, as the solution space is quite large. The entirety of the GFT needs to be trained simultaneously, to incorporate for coupling issues, and have performance converge. Initially, a heavily optimized Genetic Algorithm (GA) was utilized.