title: Data-based controllability and observability analysis of linear discrete-time systems.
abstract: In this brief, we develop data-based methods for analyzing the controllability and observability of linear discrete-time systems which have unknown system parameters. These data-based methods will only use measured data to construct the controllability matrix as well as the observability matrix, in order to verify the corresponding properties. The advantages of our methods are threefold. First, they can directly verify system properties based on measured data without knowing system parameters. Second, our calculation precision is higher than traditional approaches, which need to identify the unknown parameters. Third, our methods have lower computational complexities when constructing the controllability and observability matrices.
authors: State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. wangzhuo12300@gmail.com
authors: Zhuo
authors: Z
authors: Wang
authors: Derong
authors: D
authors: Liu
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005246:Feedback; D016014:Linear Models; D012815:Signal Processing, Computer-Assisted