Adaptive Radial Basis Function Neural Network Controller for Autonomous Multirotors

Limits in the real-time computation of micro-processors on the one hand and high-level controllers that need precise computation in addition to implementation and compiling issues, on the other hand, have caused a great gap between control science and experiments. In this work, an adaptive RBF neural network controller is proposed to control the position and attitude of an autonomous multirotor. The controller is combined with an EKF observer and simulated in real-time flight conditions. In order to check the capabilities of the system, the proposed structure has been successfully applied to a quadrotor and a hexarotor using three data types for getting similar real-time flight results. Based on the results, the proposed structure has accomplished two separate missions with different scenarios, though there exists some error, especially in position estimations which are caused by step-wise characteristics of the desired path.