Informativity of Data-Knowledge Pairs for Lyapunov Equations
For researchers in data-driven control, this work provides a theoretical framework to incorporate prior knowledge into data-based analysis, though it is an incremental extension of existing informativity concepts.
This paper extends the concept of data informativity to data-knowledge pairs for solving Lyapunov equations, deriving an algebraic condition for joint informativity and providing insights for a special case of prior knowledge.
In the past few years, data informativity with prior knowledge has attracted increasing attention. This line of research aims to characterize a dataset on a dynamical system that enables system analysis or design only by the dataset and given prior knowledge on the system. In this paper, we investigate such a characterization for the data-driven problem of computing a unique solution to Lyapunov equations. First, we introduce a notion of joint informativity for data-knowledge pairs as an extension of the standard informativity concept. Second, we derive an algebraic equivalent condition for the joint informativity. Finally, we provide further insights into the joint informativity by considering a special case of prior knowledge. The characterization presented in this paper is developed for a wide class of prior knowledge, enabling the incorporation of various forms of system information.