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-σ 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 results and also it is more suitable for the under-consideration problem.

中文翻译：

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使用无味卡尔曼滤波器的飞艇空气动力学模型估计

飞艇模型由空气静力，空气动力，动力和推进力和扭矩组成。除其他外，空气动力和扭矩的计算很困难。通常，使用风洞实验和势流理论进行计算。但是，这些方法的局限性给它们的精确计算带来了困难。在这项工作中，提出了基于无味卡尔曼滤波器（UKF）的在线估计方案进行计算。所提出的方法为完整的空气动力学模型引入了六个辅助状态。UKF使用扩展模型，并提供完整状态向量以及辅助状态的估计。所提出的方法使用最小辅助状态变量来近似完整的空气动力学模型，从而使其计算强度降低。UKF的估计性能是通过为塔西拉工程技术大学（UETT）飞艇开发非线性仿真环境来评估的。通过基于估计误差和2-σ不确定性界限执行误差分析来确定估计器性能。对于相同的问题，还实施了扩展卡尔曼滤波器（EKF），并将其结果与UKF进行了比较。仿真结果表明，UKF成功地估计了空气动力学模型产生的力和扭矩，且估计误差较小，与EKF的对比分析表明UKF改进了估计结果，也更适合考虑不足的问题。通过基于估计误差和2-σ不确定性界限执行误差分析来确定估计器性能。对于相同的问题，还实施了扩展卡尔曼滤波器（EKF），并将其结果与UKF进行了比较。仿真结果表明，UKF成功地估计了空气动力学模型产生的力和扭矩，且估计误差较小，与EKF的对比分析表明UKF改进了估计结果，也更适合考虑不足的问题。通过基于估计误差和2-σ不确定性界限执行误差分析来确定估计器性能。对于相同的问题，还实施了扩展卡尔曼滤波器（EKF），并将其结果与UKF进行了比较。仿真结果表明，UKF成功地估计了空气动力学模型产生的力和扭矩，且估计误差较小，与EKF的对比分析表明UKF改进了估计结果，也更适合考虑不足的问题。