论文标题
数据驱动的方法进行不确定性传播和可达到性分析的动力学分析
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems
论文作者
论文摘要
在本文中，我们提出了一种数据驱动的方法，用于动态系统中的不确定性传播和可达性分析。所提出的方法依赖于使用线性Perron-Frobenius（P-F）和Koopman操作员的非线性系统的线性提升。不确定性可以根据概率密度函数的力矩来表征。我们演示了P-F和Koopman操作员如何用于传播时刻。时间序列数据用于线性运算符的有限维近似，从而实现数据驱动的方法进行矩传中。提出了模拟结果以证明所提出的方法的有效性。
In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F) and Koopman operators. The uncertainty can be characterized in terms of the moments of a probability density function. We demonstrate how the P-F and Koopman operators are used for propagating the moments. Time-series data is used for the finite-dimensional approximation of the linear operators, thereby enabling data-driven approach for moment propagation. Simulation results are presented to demonstrate the effectiveness of the proposed method.
