Mixed domain analysis of MIMO dynamic interactions

Salgado, Mario E.; Yuz, Juan I.

doi:10.1109/icnsc.2007.372802

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…However, the number of configurations to be evaluated can dramatically be reduced by considering only the configurations which satisfy the three last inequalities in Eq. (13). More explicitly, the last two inequalities can first be used to fix values of Θ, limiting the number of candidate configurations to be generated, and later we can screen the candidate configurations to preserve only those with full structural rank.…”

Section: B New Methods For Automatic Control Configuration Selectionmentioning

confidence: 99%

“…The method has been derived from previous results in [13] where a biased estimation of the P M was introduced. The estimation of PM requires of a prior non-parametric estimation of the impulse response of the system which is now described.…”

Section: Online Control Configuration Selectionmentioning

confidence: 99%

“…Given a multivariable discrete time system, the value trace(P j Q i ) can be computed as (see [13]) :…”

Section: B Estimation Of P M In the Time Domainmentioning

confidence: 99%

“…Despite the clear advantages, there is little previous work concerting the estimation of IMs, including the estimation of the RGA in [17] and P M in [13]. The estimation results presented here include bounds on the estimation for robust CCS.…”

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confidence: 99%

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Online automatic and robust control configuration selection

Arranz

Birk

2017

2017 25th Mediterranean Conference on Control and Automation (MED)

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Abstract-This paper presents a complete method for automatic and robust control configuration selection for linear systems which relies upon acquired process data under gaussian noise excitation.The selection of the configuration is based on the estimation of the Interaction Measure named Participation Matrix. This estimation is derived with uncertainty bounds, which allows to determine online whether the uncertainty is sufficiently low to derive a robust decision on the control configuration to be used or if the uncertainty should be reduced by e.g. prolonging the experiment to obtain more data.

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Section: B New Methods For Automatic Control Configuration Selectionmentioning

confidence: 99%

Section: Online Control Configuration Selectionmentioning

confidence: 99%

“…Given a multivariable discrete time system, the value trace(P j Q i ) can be computed as (see [13]) :…”

Section: B Estimation Of P M In the Time Domainmentioning

confidence: 99%

mentioning

confidence: 99%

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