In this paper, the results on the parameterization of all the unfalsified models for single-input/single-output systems are extended to multi-input/multi-output systems. In this arameterization, the assumed a priori plant and noise information are upper bounds of the Hx-norm of the plant matrix-valued transfer function and the noise magnitude. The parameterization is on the basis of several series of plant time-domain identification experiment data. It is shown that for multi-input/multi-output systems, all the unfalsified plant matrix-valued transfer functions can still be expressed by the linear fractional transformation of a fixed matrix-valued transfer function and an Hx-norm bounded structure fixed, but uncertain matrix-valued transfer function. However, the high dimensions of the matrix-valued transfer functions involved in the parameterization hamper the direct application of these results to robust controller design. To overcome these difficulties, further efforts are needed.

This paper investigates estimating the frequency response of the normalized coprime factors of a possibly unstable transfer function from closed-loop frequency domain experimental data. A stochastic framework has been adopted. The dual Youla

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 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, 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.