Data generated from dynamical systems with unknown dynamics enable the learning of state observers that are: robust to modeling error, computationally tractable to design, and capable of operating with guaranteed performance. In this paper, a modular design methodology is formulated, that consists of three design phases: (i) an initial robust observer design that enables one to learn the dynamics without allowing the state estimation error to diverge (hence, safe); (ii) a learning phase wherein the unmodeled components are estimated using Bayesian optimization and Gaussian processes; and, (iii) a re-design phase that leverages the learned dynamics to improve convergence rate of the state estimation error. The potential of our proposed learning-based observer is demonstrated on a benchmark nonlinear system. Additionally, certificates of guaranteed estimation performance are provided.

翻译：动态系统产生的数据具有未知动态性能,使得国家观察员能够学习以下内容:强到建模错误,可计算到设计,并能够以有保证的性能运作。在本文件中,设计了一个模块设计方法,包括三个设计阶段:(一) 初始强到的动态设计,使一个人能够学习动态,而不允许国家估计错误发生差异(因此,安全);(二) 学习阶段,利用巴耶西亚优化和高西亚流程估算未建模的组成部分;(三) 重新设计阶段,利用所学的动态来提高国家估计误差的趋同率。我们拟议的基于学习的观察员的潜力在基准的非线性系统中得到展示。此外,还提供保证估算绩效证书。