Robotic airship mission path tracking control based on human operator's skill

Abstract

A yawing controller based on artificial neural networks (ANN) and human operator's skill is presented for robotic airship mission path tracking. Firstly, consideration of the path tracking errors from the point of view of operators is presented. Then, a data acquisition system is designed to collect flight data under manual control. Thirdly, The processed flight data are used to train and validate a multilayer feedforward ANN offline. Lastly, the trained ANN is reconstructed in the flight control system for yawing control. The experimental results indicate that this solution is valid and the ANN controller is robust even with wind disturbance.