The problem of parameterizing all unfalsified plant models from closed-loop time-domain dentification experimental data is investigated in this paper. The assumed a priori plant nformation is an upper bound of the Lx norm and the number of the unstable poles of its transfer function. Moreover, it is assumed that magnitude bounds of the disturbances that perturb the closed-loop system have been supplied. Under the condition that the stabilizing controller is known, it is shown that all the unfalsified plant transfer functions can be parameterized by a linear fractional transformation of a fixed transfer-function matrix and an Lx-norm-bounded, structure-fixed but uncertain transfer-function matrix. These results are very similar to those when plant open-loop time-domain identification experimental data are supplied. However, further efforts are still needed in order to apply these results to robust controller design. This results mainly from the computational omplexity and the high dimensions of the transfer-function matrices involved in the arameterization.

In this paper, we investigate the structure of the transfer function set which includes all eliminate the transfer functions deduced from the plant available information. It is shown that when an upper bound of the plant transfer function's H∞-norm has been supplied, and the noise contaminating the time domain identification experiment data is not too significant, such a transfer function set can be parameterized by a linear fractional transformation of two transfer function matrices. One of them is a fixed transfer function matrix which is completely determined by the plant available information and the noise magnitude. The other is a norm bounded, structure fixed, free transfer function matrix. Moreover, it is shown that the problem of analytically obtaining the fixed complexity nominal model that best approximates this transfer function set is as difficult as the μ-synthesis problem.

The problem attacked in this paper is to obtain the smallest model set which is described by nominal model. parametric uncertainty bound, unstructured iancertainty bound and consistent with a number of noise-free input-output data, under the condition that the denominator of the nominal model is prescribed. For the compatibility with the available robust control theory, the unstructured uncertainty is measured by あ∞-norm. while the parametric uncertainty is by parameter variation interval. It is shown that the unstructured uncertainty bound will generally increase if the nominal model error uncertainty is completely regarded as unstructured. Moreover. the identification problem is reduced to a convex optimization problem which is computationally tractable.

In this paper, we are concerned with a problem of robust control-oriented system identification in the time domain. Based on the well-known Schur-Takagi-AAK Theorem. we propose a linear algorithm to obtain the nominal model of the plant to be identified and the minimal bound of the uncertainty of the nominal model error which is measured by-norm. It is also shown that, in the model set defined by the nominal model and the uncertainty bound, there exists at least one model which matches the prescribed input-output data given in the time domain.

This paper deals with how to use H∞ optimization theory for the controller design of a two-axis magnetic suspension, flexible-beam system, which is a simplified model for a flexible magnetic bearing system. Both simulation and experimental results show that the designed system not only has good performance in robustness to the plant's parametric uncertainty and the unmodelled dynamics in the beam's vibration modes, mode shapes, etc., but also has good performance in response characteristics.