In the paper, a family of novel real-time tuning methods for an unmanned aerial vehicle (UAV) altitude controller in in-flight conditions. The methods allow the controller’s gains to be adapted only on the basis of measurements from a basic sensory equipment and by constructing the optimization cost function in an on-line fashion with virtually no impeding computational complexity; in the case of the altitude controller as in this paper for a hexacopter, altitude measurements were used only. The methods are not dependent on the measurement level, and present the approach in a generally applicable form to tuning arbitrary controllers with low number of parameters. Real-world experimental flights, preceded by simulation tests, have shown which method should behave best in a noisy environment when e.g. wind disturbances act on a UAV while it is in autonomous flight. As the methods can potentially be extended to other control loops or controller types, making this a versatile, rapid-tuning tool. It has been shown that a well-tuned controller using the proposed AL-TUNE scheme outperforms controllers that are tuned just to stabilize the system. AL-TUNE provides a new way of using UAVs in terms of adaptivity to changing their dynamic properties and can be deployed rapidly. This enables new applications and extends the usability of fully autonomous UAVs, unlike other tuning methods, which basically require the availability of a UAV model. The core difference with respect to other research from the field is that other authors either use a model of a UAV to optimize the gains analytically or use machine learning techniques, what increases time consumption, whereas the presented methods offer a rapid way to tune controllers, in a reliable way, with deterministic time requirements.

在本文中，一系列新颖的实时调谐方法适用于飞行条件下的无人驾驶飞行器 (UAV) 高度控制器。该方法允许控制器的增益仅基于来自基本传感设备的测量值并通过以几乎不妨碍计算复杂性的在线方式构建优化成本函数来调整；在本文中用于六轴飞行器的高度控制器的情况下，仅使用高度测量。这些方法不依赖于测量级别，并且以普遍适用的形式呈现该方法以调整具有少量参数的任意控制器。在模拟测试之前的真实世界的实验飞行已经表明哪种方法在嘈杂的环境中表现最好，例如 无人机在自主飞行时受到风的干扰。由于这些方法有可能扩展到其他控制回路或控制器类型，使其成为一种通用的快速调整工具。已经表明，使用所提出的 AL-TUNE 方案的经过良好调谐的控制器优于仅用于稳定系统的控制器。AL-TUNE 提供了一种使用无人机的新方法，它可以适应不断变化的动态特性，并且可以快速部署。与其他基本上需要 UAV 模型可用性的调整方法不同，这实现了新的应用程序并扩展了完全自主的 UAV 的可用性。与该领域其他研究的核心区别在于，其他作者要么使用无人机模型来分析优化收益，要么使用机器学习技术，