Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (6): 1318-1329.doi: 10.23919/JSEE.2020.000102


Airship aerodynamic model estimation using unscented Kalman filter

Muhammad WASIM*(), Ahsan ALI()   

  1. 1 Department of Electrical Engineering, University of Engineering and Technology, Taxila 47080, Pakistan
  • Received:2019-11-20 Online:2020-12-18 Published:2020-12-29
  • Contact: Muhammad WASIM;
  • About author:|WASIM Muhammad was born in 1990. He received his M.S. degree in electrical engineering from Electrical and Mechanical Engineering College, National University of Science and Technology, Islamabad, Pakistan. Currently, he is a Ph.D. student at the University of Engineering and Technology, Taxila, Pakistan. His research interest is control systems. E-mail:||ALI Ahsan was born in 1977. He received his Ph.D. degree in control systems from Hamburg University of Technology, Germany. Currently, he is working as an assistant professor with the Department of Electrical Engineering at the University of Engineering and Technology, Taxila, Pakistan. His research interests include machine learning, model identification, linear parameter varying modeling, and control design. E-mail:


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 validated by performing the error analysis based on estimation error and 2- $ \sigma $ 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.

Key words: airship, unscented Kalman filter (UKF), extend Kalman filter (EKF), state estimation, aerodynamic model estimation