Stable Gaussian process based tracking control of Lagrangian systems
Stable Gaussian process based tracking control of Lagrangian systems
复制标题
基于稳定高斯过程的拉格朗日系统跟踪控制
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
S. Hirche
中科院分区：
文献类型：
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作者：
Thomas Beckers;Jonas Umlauft;D. Kulić;S. Hirche
高性能的跟踪控制只能实现，如果一个好的模型的动态是可用的。然而，这样的模型通常很难仅从一阶物理获得。在本文中，我们开发了一个数据驱动的控制律，确保闭环稳定的拉格朗日系统。为此，我们使用高斯过程回归来对系统的未知动态进行前馈补偿。反馈部分的增益根据学习模型的不确定性进行调整。因此，只要学习模型足够精确地描述真实系统，反馈增益就保持较低。我们展示了如何选择一个合适的增益自适应法，结合模型的不确定性，以保证全局有界的跟踪误差。一个机器人操作器的仿真证明了所提出的控制律的有效性。
High performance tracking control can only be achieved if a good model of the dynamics is available. However, such a model is often difficult to obtain from first order physics only. In this paper, we develop a data-driven control law that ensures closed loop stability of Lagrangian systems. For this purpose, we use Gaussian Process regression for the feedforward compensation of the unknown dynamics of the system. The gains of the feedback part are adapted based on the uncertainty of the learned model. Thus, the feedback gains are kept low as long as the learned model describes the true system sufficiently precisely. We show how to select a suitable gain adaption law that incorporates the uncertainty of the model to guarantee a globally bounded tracking error. A simulation with a robot manipulator demonstrates the efficacy of the proposed control law.