论文标题
使用高斯过程的未知非线性系统的控制障碍功能
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes
论文作者
论文摘要
本文着重于未知的非线性系统的控制器合成，同时确保安全限制。我们的方法包括两个步骤，一个学习步骤，该步骤使用高斯过程和基于控制屏障功能的控制器综合步骤。在学习步骤中，我们使用使用高斯流程的数据驱动方法来学习未知的控制仿射非线性动力学以及统计学上学习模型的准确性的统计。在第二个控制器合成步骤中，我们开发了一种系统的方法来计算控制屏障功能，该功能明确考虑了学习模型的不确定性。控制屏障功能不仅会导致构造的安全控制器，而且还为安全规范满意的可能性提供了严格的下限。最后，我们通过为喷气发动机示例合成安全控制器来说明拟议结果的有效性。
This paper focuses on the controller synthesis for unknown, nonlinear systems while ensuring safety constraints. Our approach consists of two steps, a learning step that uses Gaussian processes and a controller synthesis step that is based on control barrier functions. In the learning step, we use a data-driven approach utilizing Gaussian processes to learn the unknown control affine nonlinear dynamics together with a statistical bound on the accuracy of the learned model. In the second controller synthesis steps, we develop a systematic approach to compute control barrier functions that explicitly take into consideration the uncertainty of the learned model. The control barrier function not only results in a safe controller by construction but also provides a rigorous lower bound on the probability of satisfaction of the safety specification. Finally, we illustrate the effectiveness of the proposed results by synthesizing a safety controller for a jet engine example.
