An Adaptive Neural Network State Estimator for Quadrotor Unmanned Air Vehicle
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 2
Abstract
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.
Authors and Affiliations
Jiang Yuning, Muhammad Ahmad Usman Rasool, Qian Bo, Ghulam Farid, Sohaib Tahir Chaudary
Keywords
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- EP ID EP468379
- DOI 10.14569/IJACSA.2019.0100242
- Views 87
- Downloads 0
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