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.

与受空气动力表面控制的无人机相比，质量致动的无人机具有更高的空气动力学效率和更好的隐身性能。本文着重于带有内部滑块的质量致动固定翼无人机（MFUAV）的动力学和姿态控制。基于推导的MFUAV数学模型，分析了滑块参数对动力学行为的影响，给出了滑块的理想安装位置。此外，揭示了对于低速无人机，质量驱动方案具有更高的控制效率。针对动力学的耦合，不确定性和扰动，提出了一种基于模糊系统，RBF神经网络和滑模控制的自适应滑模控制器。RBF神经网络使用最小参数学习方法来提高其学习速度。Lyapunov方法证明所提出的控制器最终是均匀有界的。最后，仿真验证了控制器的有效性和鲁棒性。