Applications of Adaptive Neural Network Control to an Unmanned Airship

Abstract

This paper represents an application of a neural network-based adaptive control to the Stability and Control Augmentation System(SCAS) of an unmanned airship whose maneuvers consist of diverse flight phases at low speeds. The neural network (NN) based adaptive SCAS is based on the inversion of a linear model of the airship at a nominal operating point and the adaptation of neural networks to unmodeled dynamics, parameter variations, and uncertain environments. This paper also presents an evaluation of the adaptive SCAS with flight test results and simulation results. In this evaluation, an outer-loop control is used. The autopilot is designed using a classical PID control algorithm for trajectory line tracking and altitude hold modes. Moreover, the adaptive SCAS approach showed superiority over the classical PID design approach in terms of the gain tuning process during a flight test.