Abstract
The complex tasks such as surveillance, construction, search and rescue can benefit of the maneuverability of multirotor Micro Aerial Vehicles (MAVs) to obtain robust, cooperative system behavior and formation control is a prominent component of the these complex tasks. This work focuses on the problem of three-dimensional formation control of multirotor MAVs by using exclusively relative sensory information. It proposes a centralized Nonlinear Model Predictive Control (NMPC) approach in a leader-follower scheme. A realistic six degrees of freedom mathematical model of a multirotor MAVs is introduced and leveraged in the control laws. The formulation of the problem is performed based on NMPC and relative sensing framework with respect to local coordinate frames of the robots. This type of formulation makes the formation independent of the full knowledge of global or common reference frames and the utilization of expensive global localization sensors. Real-time Iteration (RTI) based solution to optimal control problem (OCP) is proposed by taking the novel formulation into account. An extensive scenario is designed to test and validate the strategy. Evaluation of the results suggests that satisfactory robust performance is achieved and maintained under model uncertainty and noise in local sensors and even in cases where the dynamics of the formation suddenly changes.
Abstract (translated)
监测、建造、搜索和救援等复杂任务可以利用多旋翼微型飞行器（MAV）的机动性来获得鲁棒、协同的系统行为和编队控制，是这些复杂任务的一个突出组成部分。本文主要研究了利用相对感官信息对多旋翼微型飞行器进行三维编队控制的问题。提出了一种基于前导-后导方案的集中式非线性模型预测控制方法。介绍了一个多转子微型飞行器的六自由度实际数学模型，并将其应用于控制律中。基于NMPC和相关传感框架，对机器人的局部坐标系进行了求解。这种类型的公式使得形成独立于全球或共同参考框架的全部知识和昂贵的全球定位传感器的使用。针对最优控制问题提出了一种基于实时迭代（RTI）的求解方法。设计了一个广泛的场景来测试和验证策略。对结果的评估表明，在模型不确定性和局部传感器的噪声下，甚至在地层动力学突然变化的情况下，都能获得令人满意的鲁棒性能，并能保持该性能。
URL
https://arxiv.org/abs/1904.03742
