论文标题
深度Koopman学习非线性时变系统
Deep Koopman Learning of Nonlinear Time-Varying Systems
论文作者
论文摘要
本文提出了一种数据驱动的方法，可以通过线性变化系统（LTV）近似非线性时间变化系统（NTV）的动力学，这是由Koopman操作员和深层神经网络产生的。介绍了NTVS状态与所得LTV之间的近似误差的分析。代表性NTV上的模拟表明，即使系统迅速变化，该提出的方法也会达到小近似误差。此外，在四轮驱动器的一个示例中，模拟证明了所提出的方法的计算效率。
This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach.
