论文标题

MPC具有学习剩余动态,并在全向MAV上应用

MPC with Learned Residual Dynamics with Application on Omnidirectional MAVs

论文作者

Brunner, Maximilian, Zhang, Weixuan, Roumie, Ahmad, Tognon, Marco, Siegwart, Roland

论文摘要

空中操纵的越来越多的领域通常依赖于完全驱动的或全向微型航空车(OMAV),它们可以在与环境接触时施加任意力和扭矩。控制方法通常基于无模型方法,将高级扳手控制器与执行器分配分开。如有必要,在线骚扰观察员拒绝干扰。但是,虽然是一般,但这种方法通常会产生次优控制命令,并且不能纳入平台设计给出的约束。我们提出了两种基于模型的方法来控制OMAV,同时拒绝干扰,以执行轨迹跟踪的任务。第一个通过从实验数据中学到的模型来优化扳手命令并补偿模型错误。第二个功能优化了低级执行器命令,允许利用分配无空格并考虑执行器硬件给出的约束。在现实世界实验中显示和评估了两种方法的功效和实时可行性。

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.

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