Design of an adaptive dynamic inversion-based neurocontroller for a tandem-controlled agile surface-to-air missile

Abstract

Conventional plant-model dependent controller design approaches such as gain scheduling work well for simple flight envelope and airframe geometry. For complex flight envelope and airframe the approach results in a costly exercise to obtain a high-fidelity plant model. In this study an adaptive controller design approach is taken for an agile dual aerodynamically controlled DAC missile autopilot. Adaptive controller approach does not require an exhaustive plant model and has the capability of accommodating plant uncertainty and unmodelled dynamics online. A direct model reference adaptive control MRAC is investigated with different adaptive rules for the DAC missile. A two time-scale separation dynamic inversion controller with proportional-integral controller was used as the baseline controller of the proposed MRAC controller. The two time-scale separation controller was benchmarked with a gain scheduled three loop autopilot. A radial basis function neural network RBFNN is used to approximate the unmatched uncertainty of the missile dynamics. Adaption of the uncertainties is done on the fast dynamics controller to ensure fast recovery. The uncertainty of the slow dynamics is handled with a proportional-integral PI controller. The following adaptive rules were used with the RBFNN adaptive loop recovery ALR