A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack\nof modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural\nnetwork (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature,\na derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be\ncomputationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning\nalgorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned\naerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability\nof the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the\napplicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to timevarying\nwind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time\ncontrol systems because of its computational efficiency.
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