Direct adaptive neural control of a quadrotor unmanned aerial vehicle

This paper presents the design of a direct adaptive neural network (DANN)-based feedback linearization (FBL) controller for a multi-input multi output, unstable, nonlinear and underactuated quadrotor UAV. A full system identification was performed on the quadrotor using radial basis function neural networks (RBFNN). The DANN controller was benchmarked against a conventional PD controller which was tuned using Ziegler-Nichols tuning method. The designed controller was found to be capable of accurately tracking the desired output variables simultaneously (altitude, roll, pitch, and yaw angles respectively) and also was robust to parameter variations and disturbance input.