Modified elman recurrent neural network for attitude and altitude control of heavy-lift hexacopter

Hexacopter is a member of rotor-wing Unmanned Aerial Vehicle (UAV) which has 6 six rotors with fixed pitch blades and nonlinear characteristics that cause controlling the attitude of hexacopter is difficult. In this paper, Modified Elman Recurrent Neural Network (MERNN) is used to control attitude and altitude of Heavy-lift Hexacopter to get better performance than Elman Recurrent Neural Network (ERNN). This Modified Elman Recurrent Neural Network has a self-feedback which provides a dynamic trace of the gradients in the parameter space. In the self-feedback, the gain coefficients are trained as connection weight. This connection weight could enhance the adaptability of Elman Recurrent Neural Network to the time-varying system. The flight data are taken from a real flight experiment. Results show that the Modified Elman Recurrent Neural Network can increase performance with small error and generate a better response than Elman Recurrent Neural Network.