Comparison of RBF and SHL Neural Network Based Adaptive Control

Abstract

Modern unmanned aerial vehicles (UAVs) are required to perform complex maneuvers while operating in increasingly uncertain environments. To meet these demands and model the system dynamics with a high degree of precision, a control system design known as neural network based model reference adaptive control (MRAC) is employed. There are currently two neural network architectures used by industry and academia as the adaptive element for MRAC; the radial basis function and single hidden layer neural network. While mathematical derivations can identify differences between the two neural networks, there have been no comparative analyses conducted on the performance characteristics for the flight controller to justify the selection of one neural network over the other. While the architecture of both neural networks contain similarities, there are several key distinctions which exhibit a noticeable impact on the control system's overall performance. In this paper, a detailed comparison of the performance characteristics between both neural network based adaptive control approaches has been conducted in an application highly relevant to UAVs. The results and conclusions drawn from this paper will provide engineers with tangible justification for the selection of the better neural network adaptive element and thus a controller with better performance characteristics.