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.

翻译：本文件审查了微型航空车辆模型预测控制战略的设计和应用情况,特别是象石化器等多色配置,根据对线性或非线性动态进行优化的控制法、国家与输入限制的整合、可能的防故障设计,如果使用了强化学习方法,如果控制器提到自由飞行或实物互动或载荷运输等其他任务,本文件将审查微航空车辆模型预测控制战略的设计和应用情况,并提出一套选定的比较结果,为选择线性和非线性计划、对预测地平线的调整、基于扰动观察员的无抵消性跟踪的重要性以及这类方法对参数不确定性的内在坚固性提供洞察力。此外,还概述了关于综合应用现代深度强化学习技术和多色车辆模型预测控制的最新研究趋势。最后,本审查报告最后明确讨论了某些可提供适用于各种微电子车辆配置的离子模型预测控制功能的公开软件包。