This study addresses the design and analysis of distributed formation tracking control with disturbance rejection capability for a group of quadrotors that are perturbed by time-varying external disturbances. The proposed approach consists of two control loops: inner-loop and outer-loop control. In the inner-loop control, feedback linearization (FL) of the quadrotor system in the presence of disturbance yields linear equations with unknown disturbance parts. The unknown disturbance parts are estimated in the outer-loop control by using a disturbance observer (DO). Since the DO produces an estimation error or called residual error when estimating time-varying disturbance, this study introduces a radial basis function neural network (RBFNN) with adaptive weight to effectively approximate and eliminate the residual error. Then, to complete the formation mission, a consensus-based algorithm is implemented in the outer-loop control to enable the distributed formation tracking. The capabilities of formation tracking and online approximating are proved via the Lyapunov approach. Simulation of the quadrotor formation in the presence of time-varying wind disturbance was conducted to evaluate the effectiveness of the approach where the results obtained illustrate the robustness of the quadrotor formation towards the disturbances.

该研究针对具有时变外部扰动的四旋翼群具有扰动抑制能力的分布式编队跟踪控制的设计和分析。所提出的方法包括两个控制回路：内部回路和外部回路控制。在内环控制中，在有扰动的情况下四旋翼系统的反馈线性化（FL）会产生带有未知扰动部分的线性方程。通过使用干扰观测器（DO）在外环控制中估算未知干扰部分。由于DO在估计时变干扰时会产生估计误差或称为残差，因此本研究引入了具有自适应权重的径向基函数神经网络（RBFNN），可以有效地估计和消除残差。然后，为了完成编队任务，在外环控制中实施了基于共识的算法，以实现分布式编队跟踪。地层跟踪和在线逼近的能力已通过Lyapunov方法得到证明。在存在时变风扰的情况下对四旋翼编队进行了仿真，以评估该方法的有效性，其中获得的结果说明了四旋翼编队对扰动的鲁棒性。