The growing field of aerial manipulation often relies on fully actuated or omnidirectional micro aerial vehicles (OMAVs) which can apply arbitrary forces and torques while in contact with the environment. Control methods are usually based on model-free approaches, separating a high-level wrench controller from an actuator allocation. If necessary, disturbances are rejected by online disturbance observers. However, while being general, this approach often produces sub-optimal control commands and cannot incorporate constraints given by the platform design. We present two model-based approaches to control OMAVs for the task of trajectory tracking while rejecting disturbances. The first one optimizes wrench commands and compensates model errors by a model learned from experimental data. The second one optimizes low-level actuator commands, allowing to exploit an allocation nullspace and to consider constraints given by the actuator hardware. The efficacy and real-time feasibility of both approaches is shown and evaluated in real-world experiments.
翻译:不断增长的空中操纵领域往往依赖完全起动或全向微型飞行器(OMAVs),这些飞行器在与环境接触时可以使用任意力和托盘。控制方法通常基于无模式方法,将高级扳手控制器与一个动因分配分开;必要时,干扰被在线扰动观察员拒绝。然而,这种方法虽然一般而言,往往产生次优控制命令,不能包括平台设计带来的限制。我们为轨迹跟踪任务提出了两种基于模型的方法,前者是优化扳手命令,用从实验数据中学习的模型来补偿模型错误。第二种方法优化了低级别操作器命令,允许利用无空间分配,并考虑动作硬件带来的限制。两种方法的功效和实时可行性在现实世界实验中得到显示和评价。