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- W2130598976 abstract "In this paper, we consider indirect adaptive pole-placement control (APPC) of linear multivariable stochastic systems. Instead of the canonical representation often used in the literature, we propose using a non-minimal but otherwise uniquely identifiable pseudo-canonical parameterization that is more suitable for multivariable ARMAX model identification. To identify the plant, we use the weighted extended least-squares (WELS) algorithm, a least-squares method with slowly decreasing weights which was introduced in Bercu (1995). The pole-placement controller parameters are then calculated by using a certain perturbation of the parameter estimates such that the linear models corresponding to the perturbed estimates are uniformly controllable and observable. We prove that with a reasonable amount of prior information, the resulting APPC scheme is globally stabilizing and asymptotically self-tuning regardless of the degree of persistency of external excitation. These results represent the most complete study of stochastic multivariable APPC systems to this date." @default.
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- W2130598976 date "1997-01-01" @default.
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- W2130598976 title "Indirect adaptive pole-placement control of MIMO stochastic systems: self-tuning results" @default.
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- W2130598976 doi "https://doi.org/10.1109/9.553686" @default.
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