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An airship is a lighter than air, aerial vehicle whose model is based on dynamic, aerodynamic, aerostatic and propulsion forces and torques. Apart from other, aerodynamic forces and toques are difficult to measure. In this work, an estimation scheme for aerodynamic forces and torques based on the Extended Kalman Filter (EKF) is presented. It is assumed that the airship attitude and position estimates are available. EKF estimates the airship body axes linear and angular velocities and aerodynamic forces and torques. As the method measures a complete aerodynamic model instead of measuring its individual parameters by utilizing minimum auxiliary state variables, it is computationally non-intensive and can provide online aerodynamic model information that can be used in controller implementation in a real-time environment. Nonlinear simulation environment is developed for the experimental airship and EKF performance is evaluated. For validating the estimator's performance, 3-σ uncertainty bounds and error analysis, estimator convergence analysis and it's closed-loop simulations with Sliding Mode Controller have been performed. The simulation results show that EKF successfully estimates the airship states and aerodynamic forces and torques with minimum estimation error enhancing the model-based nonlinear controller performance.INDEX TERMS Airship, extended Kalman filter, aerodynamic model estimation, state estimation.

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No abstract

An airship model is made-up of aerostatic, aerodynamic, dynamic, and propulsive forces and torques. Besides others, the computation of aerodynamic forces and torques is difficult. Usually, wind tunnel experimentation and potential flow theory are used for their calculations. However, the limitations of these methods pose difficulties in their accurate calculation. In this work, an online estimation scheme based on unscented Kalman filter (UKF) is proposed for their calculation. The proposed method introduces six auxiliary states for the complete aerodynamic model. UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states. The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive. UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology, Taxila (UETT) airship. Estimator performance is vali dated by performing the error analysis based on estimation error and 2-uncertainty bound. For the same problem, the extended Kalman filter (EKF) is also implemented and its results are compared with UKF. The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation re sults and also it is more suitable for the under-consideration problem.