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.

This paper deals with the problem of obtaining a plant nominal model and its error uncertainty bound when a batch of plant models is provided. While the selection of model set structure is essential and probably depends on the model intended application, in this paper it is assumed that a nominal model is perturbed by its error through a homographic transformation. A necessary and sufficient condition is obtained for a nominal model to be suboptimal. Moreover, an algorithm is proposed to obtain a nominal model which is suboptimal and has a low complexity. Furthermore, the extraction of structured nominal model errors is discussed. The efficiency of the proposed model set determination algorithm is verified through the application to the simultaneous spiral control of two compact disc players.

This paper deals with the problem of estimating the probability under which a model set is not falsified by a set of measured plant frequency response samples. A definition of sample unfalsified probability has been proposed, and an explicit formula has been derived. Computation issues are also discussed. Moreover, an ecient algorithm has been developed for sample unfalsified probability calculation. Monte Carlo simulations show that the defined sample unfalsi-fied probability is appropriate in the evaluation of the quality of a model set. Compared with the deterministic approach, simulation results suggest that the probabilistic approach is more suitable in model set validation.

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.