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Neural and Fuzzy Based Nonlinear Flight Control for an Unmanned Quadcopter

The aim of this paper is to optimize the performance of a Quadcopter by designing neural and fuzzy controllers. After modeling the Quadcopter dynamics using the Newton Euler formalism, three intelligent controllers are suggested. Firstly, four Reference Model based Neural Networks (RMNN) are used to stabilize three attitudes (roll, pitch, and yaw) and the altitude displacement. In this case, each neural controller first learns to provide a control law allowing the Quadcopter to follow a chosen Reference Model. Taking into account the four flight parameters above, we will attempt in the second approach to replace four Cooperative PDs with four Neural Networks (CPDNN) whereby each Neural Network mimics the behavior of a PD controller. The third approach includes Fuzzy logic to adjust PID controllers’ gains. Finally, disturbances were taken into account to test the robustness of the controllers. Simulation results confirm the effectiveness of the proposed methods.