Improving conventional longitudinal missile autopilot using cerebellar model articulation controller neural networks

Abstract

A way to integrate the cerebellar model articulation controller (CMAC) neural network with the missile longitudinal conventional feedback controller (CFC) to compensate for nonlinearities, unmodeled dynamics, parameter variations, etc., is proposed. The inner loop of the CFC will essentially be left unchanged to improve the stability of the missile. The outer loop of CFC, in addition to playing its traditional role, would work with the CMAC to learn quickly to approximate the dynamic inversion from angle of attack to control deflection, to achieve better tracking in normal acceleration. In this arrangement, the well-known CFC acts as a safety net, whereas additional performance is brought about through CMAC learning.