An Adaptive Neural Network State Estimator for Quadrotor Unmanned Air Vehicle

An adaptive neural observer design is presented for the nonlinear quadrotor unmanned aerial vehicle (UAV). This proposed observer design is motivated by the practical quadrotor where the whole dynamical model of system is unavailable. In this paper, dynamics of the quadrotor UAV system and its state space model are discussed and a neural observer design, using a back propagation algorithm is presented. The steady state error is reduced with the neural network term in the estimator design and the transient performance of the system is improved. This proposed methodology reduces the number of sensors and weight of the quadrotor which results in the decrease of manufacturing cost. A Lyapunov-based stability analysis is utilized to prove the convergence of error to the neighborhood of zero. The performance and capabilities of the design procedure are demonstrated by the Simulation results.

@article{Yuning2019,
  title     = {An Adaptive Neural Network State Estimator for Quadrotor Unmanned Air Vehicle},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {2},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Jiang Yuning and Muhammad Ahmad Usman Rasool and Qian Bo and Ghulam Farid and Sohaib Tahir Chaudary},
  doi       = {10.14569/IJACSA.2019.0100242},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100242}
}