Dynamics and Adaptive Sliding Mode Control of a Mass-Actuated Fixed-Wing UAV

Compared with UAVs controlled by aerodynamic surfaces, mass-actuated UAVs have higher aerodynamic efficiency and better stealth performance. This paper focuses on the dynamics and attitude control of a mass-actuated fixed-wing UAV (MFUAV) with an internal slider. Based on the derived mathematical model of the MFUAV, the influence of the slider parameters on the dynamical behavior is analyzed, and the ideal installation position of the slider is given. Besides, it is revealed that the mass-actuated scheme has a higher control efficiency for low-speed UAVs. To deal with the coupling, uncertainty and disturbances in the dynamics, an adaptive sliding mode controller based on fuzzy system, RBF neural network and sliding mode control are proposed. The RBF neural network uses the minimum parameter learning method to improve its learning speed. It is proved by the Lyapunov method that the proposed controller is ultimately uniformly bounded. Finally, the simulation verifies the effectiveness and robustness of the controller.