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.

In this paper, an algorithm is proposed for the identification of fractal signals with wavelet-based models from corrupted data. This algorithm is based on curve fitting and wavelet transformation. It is proved that whenever is greater than 0, the algorithm provides an almost consistent estimation. oreover, the estimated parameters are asymptotically Gaussian distributed. A mean-square asymptotic convergence rate of the estimated parameters has also been established. Simulation results verify the efficiency of the proposed algorithm.

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.

This paper investigates the consistency between frequency domain closed-loop experimental data and a model set with linear fractional unstructured uncertainties.When the number of unstable poles of the transfer function matrices (TFM) in the model set are identical, a necessary and sufficient condition is derived for the reproduction of experimental data. This condition is expressed as the non-positiveness of a quadratic form of the external disturbances and measurement errors, together with a linear constraint.In this derivation, it is not required that all the TFMs in the model set are internally stabilizable by the controller.The condition is also not necessary for the closed-loop system consisting of the nominal model and the controller to be internally stable.It is believed that this condition is theoretically and computationally attractive in model set validation, no matter whether a deterministic or stochastic framework is adopted.