Hybrid Model-Based and Data-Driven Wind Velocity Estimator for an Autonomous Robotic Airship
This work addresses wind estimation for autonomous airship control, but it is incremental as it combines existing techniques without a major breakthrough.
The paper tackled wind velocity estimation for autonomous robotic airships by proposing a hybrid model-based and data-driven estimator, which increased estimation performance in simulations compared to individual Extended Kalman Filter and Neural Network approaches.
In the context of autonomous airships, several works in control and guidance use wind velocity to design a control law. However, in general, this information is not directly measured in robotic airships. This paper presents three alternative versions for estimation of wind velocity. Firstly, an Extended Kalman Filter is designed as a model-based approach. Then a Neural Network is designed as a data-driven approach. Finally, a hybrid estimator is proposed by performing a fusion between the previous designed estimators: model-based and data-driven. All approaches consider only Global Positioning System (GPS), Inertial Measurement Unit (IMU) and a one dimensional Pitot tube as available sensors. Simulations in a realistic nonlinear model of the airship suggest that the cooperation between these two techniques increases the estimation performance.