Markov Parameters Of System Research Articles

Time moments and Markov parameters of composite systems

A theorem is given to show that a reduced-order composite system, formed from the interconnection of a number of reduced-order models that are Pade-type approximants of some subsystems, matches the initial time moments and Markov parameters of the composite system formed from the interconnection of these subsystems. Thus the reduced-order composite system has the same tracking properties as the original composite system.

A non-parametric approach to the identification of linear multivariable systems

A non-parametric approach is proposed for the identification of linear time-invariant discrete-time multivariable systems. It is based on the estimation of the Markov parameters of the system by cross-correlation between the output and a white noise input and can also be used with the system under operation if a dither signal can be added for identification. Results of simulation are included, indicating that the scheme works successfully even when the output measurements are contaminated with considerable amounts of noise.

Relationship Between State-space And Input-outputModels Via Observer Markov Parameters

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 In the stochastic case, the observer Markov parameters contain information of an optimal observer such as a Kalman filter. The observer Markov parameters can be identified directly from experimental input-output data. Making use of the new connection, one can extract from the identified observer Markov parameters not only a state-space model of the system, but also an associated observer gain for use in modern state-space based feedback controller design.