Applications of polynomial neural networks to FDIE and reconfigurable flight control

Fault detection, isolation, and estimation (FDIE) functions and reconfiguration strategies for flight control systems present major technical challenges, primarily because of uncertainties resulting from limited observability and an almost unlimited variety of malfunction and damage scenarios. Attention is focused on a portion of the problem, i.e. global FDIE for single impairments of control effectors. Polynomial neural networks are synthesized using a constrained error criterion to obtain pairwise discrimination between impaired and no-fail conditions and isolation between impairment classes. The pairwise discriminators are then combined in a form of voting logic. Polynomial networks are also synthesized to obtain estimates of the amount of effector impairment. The algorithm for synthesis of polynomial networks (ASPN) and related methods are used to create the networks, which are high-order, linear or nonlinear, analytic, multivariate functions of the in-flight observables. The authors outline the design procedure, including database preparation, extraction of waveform features, network synthesis techniques, and the architecture of the FDIE system that has been studied for control reconfigurable combat aircraft (CRCA). Single-look (25-ms response time) simulation results are presented