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A Data-driven Hierarchical Control Structure for Systems with Uncertainty
arXiv - CS - Systems and Control Pub Date : 2020-09-13 , DOI: arxiv-2009.05914
Lu Shi, Hanzhe Teng, Xinyue Kan, and Konstantinos Karydis

The paper introduces a Data-driven Hierarchical Control (DHC) structure to improve performance of systems operating under the effect of system and/or environment uncertainty. The proposed hierarchical approach consists of two parts: 1) A data-driven model identification component to learn a linear approximation between reference signals given to an existing lower-level controller and uncertain time-varying plant outputs. 2) A higher-level controller component that utilizes the identified approximation and wraps around the existing controller for the system to handle modeling errors and environment uncertainties during system deployment. We derive loose and tight bounds for the identified approximation's sensitivity to noisy data. Further, we show that adding the higher-level controller maintains the original system's stability. A benefit of the proposed approach is that it requires only a small amount of observations on states and inputs, and it thus works online; that feature makes our approach appealing to robotics applications where real-time operation is critical. The efficacy of the DHC structure is demonstrated in simulation and is validated experimentally using aerial robots with approximately-known mass and moment of inertia parameters and that operate under the influence of ground effect.

中文翻译:

不确定性系统的数据驱动分层控制结构

本文介绍了一种数据驱动的分层控制 (DHC) 结构,以提高在系统和/或环境不确定性影响下运行的系统的性能。所提出的分层方法由两部分组成:1)数据驱动的模型识别组件,用于学习提供给现有低级控制器的参考信号与不确定的时变设备输出之间的线性近似。2) 一个更高级别的控制器组件,它利用已识别的近似值并围绕系统的现有控制器来处理系统部署期间的建模错误和环境不确定性。我们为已识别的近似值对噪声数据的敏感性得出松散和严格的界限。此外,我们表明添加更高级别的控制器可以保持原始系统的稳定性。所提出的方法的一个好处是它只需要对状态和输入进行少量观察,因此它可以在线工作;该功能使我们的方法对实时操作至关重要的机器人应用程序具有吸引力。DHC 结构的功效在模拟中得到了证明,并使用具有近似已知质量和惯性矩参数的空中机器人进行了实验验证,这些机器人在地面效应的影响下运行。
更新日期:2020-09-15
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