Robotic airship mission path-following control based on ANN and human operator's skill

Robotic airships have numerous low-speed and low-altitude application potentials. Mission path following is one such application, which, however, presents an autonomy challenge. In this paper, a yawing controller, which is based on artificial neural network (ANN) and human operator skills, is proposed for mission path following of robotic airships. First, the path-following errors based on the operator's point of view are discussed. Then, a data acquisition system is designed to collect the flight data under manual control, and the data are then processed and used for offline training and validation of a multilayer feed-forward neural network. Finally, the trained neural network is reconstructed in the flight control system for yawing control, and the experimental results confirm the effectiveness of this method. It is also shown that the ANN controller is robust even with wind disturbance.