Stable Gaussian process based tracking control of Euler-Lagrange systems
Stable Gaussian process based tracking control of Euler-Lagrange systems
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DOI：
10.1016/j.automatica.2019.01.023
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发表时间：
2019-05-01
期刊：
影响因子：
6.4
通讯作者：
Hirche, Sandra
中科院分区：
文献类型：
--
作者：
Beckers, Thomas;Kulic, Dana;Hirche, Sandra
由于系统模型的不确定性和外部干扰，现实世界的欧拉-拉格朗日系统的完美跟踪控制具有挑战性。跟踪误差的大小可以通过增加反馈增益或改进系统模型来减小。后者显然更可取，因为它可以在低反馈增益下保持良好的跟踪性能。然而，准确的模型往往很难获得。在本文中，我们解决未知欧拉-拉格朗日系统的稳定高性能跟踪控制问题。特别是，我们采用高斯过程回归来获得数据驱动模型，用于系统未知动态的前馈补偿。模型保真度用于调整反馈增益，从而允许高模型置信度的状态空间区域中的低反馈增益。所提出的控制律保证了具有特定概率的全局有界跟踪误差。仿真研究证明了其优于最先进的跟踪控制方法。 (C) 2019 Elsevier Ltd. 保留所有权利。
Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances. The magnitude of the tracking error can be reduced either by increasing the feedback gains or improving the model of the system. The latter is clearly preferable as it allows to maintain good tracking performance at low feedback gains. However, accurate models are often difficult to obtain.In this article, we address the problem of stable high-performance tracking control for unknown Euler-Lagrange systems. In particular, we employ Gaussian Process regression to obtain a data-driven model that is used for the feed-forward compensation of unknown dynamics of the system. The model fidelity is used to adapt the feedback gains allowing low feedback gains in state space regions of high model confidence. The proposed control law guarantees a globally bounded tracking error with a specific probability. Simulation studies demonstrate the superiority over state of the art tracking control approaches. (C) 2019 Elsevier Ltd. All rights reserved.