Evolving Intelligent System for Trajectory Tracking of Unmanned Aerial Vehicles

This article develops an evolving type-2 quantum fuzzy neural network (eT2QFNN) control scheme for achieving trajectory tracking with unmanned aerial vehicles (UAVs). The proposed approach involves quantum membership functions, automatic rule growing process, and parameter adjustment learning scenario to deal with the problems of inadequacy, uncertainties, and noise in conventional control techniques. Furthermore, the proposed approach is operated in a parallel structure with the proportional derivative (PD) controller to compensate the transients in performance and learn the dynamic characteristics of the system. Besides, a sliding theory-based adaptive law is equipped with the control approach to compensate for the nonlinearity of the UAV. To assess the performance, numerical simulations and real-time experiments are carried for pitch and yaw axes control of two degrees of freedom (2DoF) helicopter test rig with the proposed approach. The simulations and experiments are aimed at achieving an offline path tracking with an objective to minimize the deviation error and improve the time response characteristics of the UAVs. The results depict the robustness of the proposed approach in terms of integral time absolute error for a helicopter following various trajectories.