Workshop 1: Hybrid and stochastic hybrid systems
|Speaker: Professor Andrew R. Teel|
Affiliation: University of California Santa Barbara
Abstract: In this workshop, we will present the basics of modeling for hybrid dynamical systems, including also a modeling framework for stochastic hybrid systems. Several examples will be used to illustrate the modeling framework. After modeling, we turn our attention to stability properties that are natural for hybrid systems. In turn, Lyapunov function conditions for these stability properties will also be established and illustrated. These conditions include weak Lyapunov conditions based on the invariance principle, which can be shown to hold for hybrid systems. Converse Lyapunov theorems will also be reviewed to make the case that Lyapunov functions are natural for hybrid systems. The objective of the workshop is to provide researchers with a modeling framework and analysis tools that they can use for a wide variety of control problems.
Workshop 2: Data Analytics for Control Systems Engineering
|Speaker: Professor Biao Huang|
Affiliation:University of Alberta ,Canada
Abstract: Modern industries are awash with large amount of data. Extraction of information and knowledge discovery from data for control system design, particularly from day by day routine process operating data, is especially challenging. There exist numerous challenging issues such as data nonlinearity, non-Gaussian distributions, high dimensionality, collinearity, multiple modal operations, outlying points, missing measurement etc that must be considered during the information extraction process. This presentation will discuss state-of-the-art development of data analytics to deal with these issues and to develop predictive models, soft sensors and fault detection and diagnosis monitors from data. The theory of robust data analytics is explained. The non-Gaussian behavior of process data is discussed. The concept of data analytics is illustrated in detail by applications to data based image processing. The successful use of data analytics for predictive modeling and soft sensing will be elaborated.