A Data-driven Pole Placement Method Simultaneously Identifying a State-space Model

Abstract

This paper studies a data driven pole placement method deriving a state feedback gain directly from a pair of state and input measurements of a given controllable discrete-time system. In a conventional approach, a state space model should be identified in advance to apply a standard pole placement algorithm. In the proposed method, a state feedback gain and a state space model can be simultaneously obtained under an assumption.