Most existing control methods for quadrotor unmanned aerial vehicles (UAVs) are based on the primary assumption that the center of gravity (CoG) is fixed and is in the same position as the centroid, which is not necessarily true with swing load as continuously making CoG vary with the swing angle and substantially complicating the dynamic model of UAV. This article presents an adaptive learning and fault-tolerant control scheme for quadrotor UAVs with varying CoG and unknown moment of inertia. First, we establish the dynamic model of quadrotor UAVs in the presence of time-varying CoG, input saturation, and actuator fault. Then, we design a fault-tolerant adaptive learning controller for the quadrotor UAVs and show that both linear and angular velocity tracking errors are ensured to converge to a residual set around zero in the presence of full-state constraints. Furthermore, all signals in the closed-loop system are uniformly ultimately bounded. Simulation studies also confirm the effectiveness of the proposed control method.

中文翻译：

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具有时变CoG和全状态约束的四旋翼无人机的容错自适应学习控制。

对于四旋翼无人机（UAV），大多数现有的控制方法都基于以下主要假设：重心（CoG）是固定的，并且与质心处于相同的位置；对于摆动负载，连续不断地产生CoG不一定是正确的。随摆角的变化而变化，并使无人机的动力学模型复杂化。本文提出了一种具有可变CoG和未知惯性矩的四旋翼无人机的自适应学习和容错控制方案。首先，在存在时变CoG，输入饱和和执行器故障的情况下，建立四旋翼无人机的动力学模型。然后，我们为四旋翼无人机设计了一种容错自适应学习控制器，并表明在存在全状态约束的情况下，确保线性和角速度跟踪误差都收敛到零附近的残差集。此外，闭环系统中的所有信号最终均一地受到限制。仿真研究也证实了所提出的控制方法的有效性。