Nonlinear Fly‐By‐Throttle H∞ Control Using Neural Networks

Abstract

ABSTRACTA neural network approach to gain scheduling H∞ controllers for propulsion controlled aircraft (PCA) systems is introduced. The PCA system is applied to backup control of aircraft experiencing control surface failure. The H∞ technology is applied to the problem of matching the crippled aircraft and the nominal model. Various H∞ controllers at various flight conditions are used to train radial basis function networks (RBFN), which can then be used as the nonlinear controller. Simulation on an L‐1011 under fly‐by‐throttle control demonstrates that the RBFN controller can stabilize the crippled airplane to obtain the desired model and possesses robustness against the engine delay.