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This paper proposes an adaptive dynamic programming-based adaptive-gainsliding mode control (ADP-ASMC) scheme for a fixed-wing unmanned aerial vehicle(UAV) with matched and unmatched disturbances. Starting from the dynamic offixed-wing UAV, the control-oriented model composed of attitude subsystem andairspeed subsystem is established. According to the different issues in twosubsystems, two novel adaptive-gain generalized super-twisting (AGST)algorithms are developed to eliminate the effects of disturbances in twosubsystems and make the system trajectories tend to the designed integralsliding manifolds (ISMs) in finite time. Then, based on the expected equivalentsliding-mode dynamics, the modified adaptive dynamic programming (ADP) approachwith actor-critic (AC) structure is utilized to generate the nearly optimalcontrol laws and achieve the nearly optimal performance of the sliding-modedynamics. Furthermore, through the Lyapunov stability theorem, the trackingerrors and the weight estimation errors of two neural networks (NNs) are alluniformly ultimately bounded (UUB). Finally, comparative simulationsdemonstrate the superior performance of the proposed control scheme for thefixed-wing UAV.

Adaptive dynamic programming-based adaptive-gain sliding mode tracking control for fixed-wing UAV with disturbances