This paper presents a solution for the state estimation and control problems for a class of unconventional vertical takeoff and landing (VTOL) UAVs operating in forward-flight conditions. A tightly-coupled state estimation approach is used to estimate the aircraft navigation states, sensor biases, and the wind velocity. State estimation is done within a matrix Lie group framework using the Invariant Extended Kalman Filter (IEKF), which offers several advantages compared to standard multiplicative EKFs traditionally used in aerospace and robotics problems. An SO(3)- based attitude controller is employed, leading to a single attitude control law without a separate sideslip control loop. A control allocator is used to determine how to use multiple, possibly redundant, actuators to produce the desired control moments. The wind velocity estimates are used in the attitude controller and the control allocator to improve performance. A numerical example is considered using a sample VTOL tailsitter-type UAV with four control surfaces. Monte-Carlo simulations demonstrate robustness of the proposed control and estimation scheme to various initial conditions, noise levels, and flight trajectories.

翻译：本文针对一类非常规垂直起降（VTOL）无人机在前飞状态下的状态估计与控制问题提出了解决方案。采用紧耦合状态估计方法估计飞行器导航状态、传感器偏差及风速。状态估计在矩阵李群框架内利用不变扩展卡尔曼滤波器（IEKF）实现，相比航空航天与机器人领域传统使用的标准乘性扩展卡尔曼滤波器（EKF），该框架具有多项优势。采用基于SO(3)的姿态控制器，无需独立的侧滑角控制回路，即可实现单一姿态控制律。通过控制分配器确定如何利用多个（可能冗余的）执行器产生期望的控制力矩。风速估计值被用于姿态控制器及控制分配器以提升性能。以配备四个控制面的典型VTOL尾座式无人机为例进行数值仿真。蒙特卡洛仿真验证了所提控制与估计方案对不同初始条件、噪声水平及飞行轨迹的鲁棒性。