This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations.

中文翻译：

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微型飞行器的模型预测控制：一项调查

本文介绍了微型飞机模型预测控制策略的设计和应用综述，尤其是多旋翼配置，例如四旋翼。根据在线性或非线性动力学上优化的控制定律，状态和输入约束的集成，可能的容错设计，是否已使用强化学习方法以及如果控制器引用的控制律来组织领域中的各种工作集。自由飞行或其他任务，例如物理互动或货物运输。还将展示一组选定的比较结果，这些结果可为深入了解线性和非线性方案，调整预测范围，基于干扰观测器的无偏移跟踪的重要性以及此类方法对参数不确定性的固有鲁棒性。此外，概述了有关现代深度强化学习技术和多旋翼飞行器模型预测控制的组合应用的最新研究趋势。最后，本篇综述以对选定的开源软件包的明确讨论作为结束，这些软件包提供了适用于各种微型航空器配置的现成的模型预测控制功能。