Altitude identification and intelligent control of a flapping wing micro aerial vehicle using modified generalized regression neural networks

Abstract

The design of an intelligent controller for the Flapping Wing Micro Aerial Vehicle (FWMAV) is addressed in this paper. Generalized Regression Neural Network (GRNN) is used for the identification and control. One of the main issues associated with the GRNN is the growth in the size of the hidden layer with the data size increase is addressed in this research. The superiority of the GRNN controller is shown using numerical simulations in the presence of disturbances and the performance is compared with a classical Proportional Integral Derivative (PID) controller.