Filtering and Estimation of State and Wind Disturbances Aiming Airship Control and Guidance

Good state and wind estimation is a requirement for the development of guidance and control techniques for airships. However, usually this information is not directly available from the airship sensors. The typical solution applies filtering, estimation and sensor fusion methods. This paper presents a comparative study, evaluating three solutions for the state estimation of NOAMAY airship. We also present alternative versions for the crucial estimation of the wind velocity, combining Kalman filters with a data-driven Neural Network. Finally, we present special solutions to usual problems encountered in filtering implementation as the mitigation of delays caused by second-order filters. The sensors set considered is composed of a global positioning system, an inertial measurement unit and a one-dimensional Pitot probe. Comparative simulation results are presented with the use of a realistic nonlinear model of the airship.