This article addresses theoretical and practical challenges associated with a commercially available and ready-to-fly small-scale unmanned aircraft system (UAS) developed by Parrot SA: the Mambo quad rotorcraft. The dynamic model and the structure of the controller running onboard the UAS autopilot are not disclosed by its manufacturers. For this reason, a novel robust controller for discrete-time systems under time delays and input saturation is first developed for this platform. Then, three fundamental estimation and control challenges are addressed. The first challenge is the system identification of the $X$ and $Y$ translational dynamics of the UAS. To accomplish this goal, input–output data pairs are collected from different UAS platforms during real-time experimental flights. A group of dynamic models are identified from the data pairs through an extended least-squares algorithm. The obtained models are similar in nature but exhibit parametrical variations due to uncertainties in the fabrication process and different levels of wear and tear. Using a time-varying modeling approach, the second challenge addresses the development of a robust controller, which guarantees the stability of all the identified dynamic models. The third challenge addresses the development of a nonlinear controller enhanced with a perturbation estimation, which can reject, from the nominal model, the effects of model uncertainties and perturbations. These theoretical developments are presented in the form of two original theorems. The proposed strategies are ultimately validated in a set of real-time experiments, demonstrating their effectiveness and applicability.

中文翻译：

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Parrot Mambo 平台具有扰动估计的鲁棒控制策略

本文解决了与 Parrot SA 开发的商用和可随时飞行的小型无人机系统 (UAS) 相关的理论和实践挑战：曼波四旋翼飞机。UAS 自动驾驶仪上运行的控制器的动力学模型和结构未由其制造商披露。出于这个原因，首先为此平台开发了一种新颖的鲁棒控制器，用于在时间延迟和输入饱和下的离散时间系统。然后，解决了三个基本的估计和控制挑战。第一个挑战是系统识别 $X$ 和 $Y$ UAS 的平移动力学。为了实现这一目标，在实时实验飞行期间从不同的 UAS 平台收集输入-输出数据对。通过扩展的最小二乘算法从数据对中识别出一组动态模型。获得的模型本质上是相似的，但由于制造过程中的不确定性和不同程度的磨损而表现出参数变化。使用时变建模方法，第二个挑战解决了鲁棒控制器的开发，这保证了所有识别的动态模型的稳定性。第三个挑战解决了通过扰动估计增强非线性控制器的开发，该控制器可以从标称模型中拒绝模型不确定性和扰动的影响。这些理论发展以两个原始定理的形式呈现。所提出的策略最终在一组实时实验中得到验证，证明了它们的有效性和适用性。