论文标题
使用贝叶斯优化的未知非线性系统的安全基于学习的观察者
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
论文作者
论文摘要
来自具有未知动力学的动力学系统生成的数据使得可以学习：对误差进行建模，可用于设计的计算障碍，并且能够以保证的性能进行操作。在本文中，制定了模块化设计方法，该方法由三个设计阶段组成：（i）最初的强大观察者设计，使一个人能够学习动力学，而无需使状态估计错误差异（因此，安全）； （ii）一个学习阶段，其中未建模的组件是使用贝叶斯优化和高斯过程估算的； （iii）一个重新设计阶段，该阶段利用学习的动力学来提高状态估计误差的收敛速率。在基准非线性系统上证明了我们提出的基于学习的观察者的潜力。此外，还提供了保证估算性能的证书。
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.
