Author(s)

M.Q. Phan & R.W. Longman

Abstract

This paper describes the relationship between two types of commonly used models in control and identification theory: state-space and input-output models. The relationship between the two model structures can be explained in terms of a newly formulated set of parameters called the observer Markov parameters. This is different from the usual connection between the two model structures via well-known canonical realizations. The newly defined observer Markov parameters generalize the standard system Markov parameters by incorporating information of an associated observer. In the deterministic case, the observer Markov parameters subsume a state-space model and a deadbeat observer gain

Keywords