论文标题
为了获得数值KKL观察者的收益调整
Towards gain tuning for numerical KKL observers
论文作者
论文摘要
本文提出了为一般非线性系统调整观察者的第一步。依赖于Kazantzis-Kravaris/Luenberger（KKL）观察者的最新结果，我们提出了一个经验标准，以通过交易瞬态性能和对测量噪声的敏感性来指导观察者的校准。我们对增益矩阵进行参数，并在观察者家族中评估该标准的不同参数值。然后，我们使用神经网络来学习观察者和非线性系统之间的映射，并提出了一种新颖的方法，可以有效地对非线性回归进行采样。我们在数值模拟中说明了这种方法的优点。
This paper presents a first step towards tuning observers for general nonlinear systems. Relying on recent results around Kazantzis-Kravaris/Luenberger (KKL) observers, we propose an empirical criterion to guide the calibration of the observer, by trading off transient performance and sensitivity to measurement noise. We parametrize the gain matrix and evaluate this criterion over a family of observers for different parameter values. We then use neural networks to learn the mapping between the observer and the nonlinear system, and present a novel method to sample the state-space efficiently for nonlinear regression. We illustrate the merits of this approach in numerical simulations.
