论文标题
数据驱动的观察者设计，用于带静态摩擦的惯性轮摆
Data-driven observer design for an inertia wheel pendulum with static friction
论文作者
论文摘要
考虑了惯性的惯性摩擦液的静态摩擦，提出了一种间接数据驱动的状态观察者设计方法。在实际实验室设置中发生的摩擦力的特征是施加效应以及两种不同的动态行为之间的过渡，即粘附和不变。这些开关非线性动力学以数据驱动的方式使用各种机器学习方法鉴定，即，无监督的分离和将测量状态轨迹的特征聚类分为两个动态类，以及对状态依赖性切换条件的监督分类。具有两个动力学的内部开关结构的确定系统与移动范围估计器结合使用。
An indirect data-driven state observer design approach for the inertia wheel pendulum considering static friction of the actuated inertia disc is presented. The frictional forces occurring in a real laboratory setup are characterized by the Stribeck effect as well as the transition between two different dynamic behaviors, sticking and non-sticking. These switching nonlinear dynamics are identified with various machine learning methodologies in a data-driven manner, i.e., the unsupervised separation and feature clustering of measured state trajectories into two dynamic classes, and the supervised classification of a state-dependent switching condition. The identified system with the interior switching-structure of two dynamics is combined with a moving horizon estimator.
