Robustness is crucial for flight vehicle control, which is directly related to control performance and mission security. Adaptive control methods are effective for handling perturbations, but most rely on expert knowledge in choosing parametric estimator structures. These uncertainties in quadrotor dynamics own complex representation form, so the structure of the estimator is difficult to determine. Our work integrates modified Gaussian process regression (GPR) with a quaternion-based command filtered backstepping framework, such that quadrotors subjected to perturbations can rapidly and accurately track the desired trajectory. GPR-based control architecture applies Bayesian nonparametric representation to estimate the distribution of perturbations; the nonparametric estimation method requires no prior knowledge about uncertainties and can inherently manage measurement noises. The zero-mean prior of GPR is modified by using the error integral, which compensates for the estimation bias of constant and/or low-frequency disturbances. Furthermore, to estimate the rapidly changing perturbations, a time-dependent form regarding state point selection is applied to increase the influence of recent measurements. Simulation results demonstrate the effectiveness of these modifications and the superiority of the proposed control system.

鲁棒性对于飞行器的控制至关重要，这与控制性能和任务安全性直接相关。自适应控制方法可有效地处理扰动，但大多数方法都依赖于专家知识来选择参数估计器结构。四旋翼动力学中的这些不确定性具有复杂的表示形式，因此很难确定估计器的结构。我们的工作将改进的高斯过程回归（GPR）与基于四元数的命令过滤反推框架进行了集成，从而使受到扰动的四旋翼飞机可以快速，准确地跟踪所需的轨迹。基于GPR的控制体系结构应用贝叶斯非参数表示来估计扰动的分布。非参数估计方法不需要有关不确定性的先验知识，并且可以固有地管理测量噪声。GPR的零均值先验通过使用误差积分进行修改，该误差积分可补偿恒定和/或低频干扰的估计偏差。此外，为了估计快速变化的扰动，应用了与状态点选择有关的时间依赖形式，以增加最近测量的影响。仿真结果证明了这些修改的有效性以及所提出的控制系统的优越性。关于状态点选择的时变形式被应用来增加最近测量的影响。仿真结果证明了这些修改的有效性以及所提出的控制系统的优越性。关于状态点选择的时变形式被应用来增加最近测量的影响。仿真结果证明了这些修改的有效性以及所提出的控制系统的优越性。