Helicopter motion control using adaptive neuro-fuzzy inference controller

This paper proposes an adaptive neuro-fuzzy inference controller using a feed forward neural network based on nonlinear regression. The general regression neural network is used to construct the base of an adaptive neuro-fuzzy system. This neural network uses a different learning capability when compared with the classical clustering algorithm. The parameters of the general regression neural network are obtained using the gradient descent and least squares algorithms. The simplification of the neuro-fuzzy architecture is done throw the elimination of the rules, maintaining the performance of the controller. In the simulation, the adaptive neuro-fuzzy controller is used to control the helicopter motion in the hover flight mode position. The longitudinal and lateral cyclic, the collective and pedals are used to enable the helicopter to maintain its position fixed in space. Results show the effectiveness of the proposed method.