Introduction
Modeling of complex multivariable plants is always subject to uncertainty in their linear models. However, in the face of unknown or uncertain multivariable plants, the control configuration of the plant may endure fundamental changes, which will severely degrade the decentralized controller performance. The well-known input-output pairing techniques described in previous sections are unable to analyze the effect of uncertainty on input-output pairing and only recently, pairing methods are proposed for uncertain multivariable plants. The approaches to control configuration selection in the presence of uncertainty can be categorized in the following classes:
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Structured uncertainties.
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Diagonal input uncertainties.
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Condition number and robustness analysis.
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Statistical description of uncertainty bound for the RGA.
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Unstructured uncertainty modeling and bounds on the magnitude of the worst-case relative gain.
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State space description of uncertain multivariable plants and the DIOPM approach.
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On-line identification and adaptive input-output pairing to encounter parameter changes.
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Khaki-Sedigh, A., Moaveni, B. (2009). Control Configuration Selection of Linear Uncertain Multivariable Plants. In: Control Configuration Selection for Multivariable Plants. Lecture Notes in Control and Information Sciences, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03193-9_7
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