This work focuses on developing a data-driven framework using Koopman operator theory for system identification and linearization of nonlinear systems for control. Our proposed method presents a deep learning framework with recursive learning. The resulting linear system is controlled using a linear quadratic control. An illustrative example using a pendulum system is presented with simulations on noisy data. We show that our proposed method is trained more efficiently and is more accurate than an autoencoder baseline.

翻译：本文聚焦于利用Koopman算子理论构建数据驱动框架，实现非线性系统的系统辨识与线性化，以服务于控制应用。所提方法提出了一种包含递归学习的深度学习框架，并采用线性二次型调节器对生成的线性系统进行控制。以摆锤系统为例，在含噪声数据仿真中展示了方法有效性。结果表明，与自编码器基线相比，所提方法训练效率更高且精度更优。