Relative disturbance gain array

Chang, Jin-Wen; Yu, Cheng‐Ching

doi:10.1002/aic.690380406

Cited by 41 publications

(42 citation statements)

References 18 publications

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“…Therefore, an augmented version of relative disturbance gain β ij can be defined and a matrix can be formed. The matrix RDGA (B) is defined as (Chang and Yu, 1992): The ijth entry, β ij , corresponds to the RDG when the ith output is paired with the jth input. Notice that, since the vector G d is involved in the computation of β ij , corresponding changes in G d have to be made in permutation in the outputs in the matrix G.…”

Section: Generalized Relative Disturbance Gain (Grdg)mentioning

confidence: 99%

“…An interaction measure GRDG is defined to evaluate the load effect under a specific controller structure (closed-loop load effect) over the open loop load effect (Chang and Yu, 1992 …”

Section: Generalized Relative Disturbance Gain (Grdg)mentioning

confidence: 99%

“…where G is the process model in IMC for defining the controller structure (detail can be found in (Chang and Yu, 1992)). …”

Section: Generalized Relative Disturbance Gain (Grdg)mentioning

confidence: 99%

“…Scheme 1: (y1-u3) (y2 -u1) (y3 -u2) (y4-u4) From the IE (integral error) standpoint, a structure giving small value in i δ is preferred (Chang and Yu, 1992). It is obvious that Scheme 4 is the most favourable pairing among the 4 schemes.…”

Section: Grdg Analysis For the Alstom Gasifiermentioning

confidence: 99%

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Control structure selection for the ALSTOM gasifier benchmark process using GRDG analysis

Agustriyanto

Zhang

2009

IJMIC

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The objective of this work is to explore the disturbance rejection capability of possible multi-loop control structures for the ALSTOM gasifier benchmark process and selecting the appropriate control structure. Generalized Relative Disturbance Gain (GRDG) analysis is used for control structure determination. In order to carry out GRDG analysis, process models in the form of transfer functions are obtained from the discrete time models identified using the Output-Error (OE) method for system identification. Models identified with the OE method can provide accurate long range prediction (or simulation) performance and, hence, lead to accurate transfer function models. The GRDG analysis results clearly show that the baseline controller proposed by Asmar et al. (Asmar et al., 2000) is the favoured multi-loop control structure among their initial designs. This study provides explanation for the reason behind the impressive performance of the ALSTOM baseline controller that is not available before.

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Section: Generalized Relative Disturbance Gain (Grdg)mentioning

confidence: 99%

“…An interaction measure GRDG is defined to evaluate the load effect under a specific controller structure (closed-loop load effect) over the open loop load effect (Chang and Yu, 1992 …”

Section: Generalized Relative Disturbance Gain (Grdg)mentioning

confidence: 99%

“…where G is the process model in IMC for defining the controller structure (detail can be found in (Chang and Yu, 1992)). …”

Section: Generalized Relative Disturbance Gain (Grdg)mentioning

confidence: 99%

Section: Grdg Analysis For the Alstom Gasifiermentioning

confidence: 99%

See 2 more Smart Citations

Control structure selection for the ALSTOM gasifier benchmark process using GRDG analysis

Agustriyanto

Zhang

2009

IJMIC

View full text Add to dashboard Cite

show abstract

“…And different properties of the RGA can be consulted at (Bristol, 1966;McAvoy, 1983;Grosdidier et al, 1985;Skogestad & Morari, 1987 a & b;Skogestad & Postlethwaite, 2005;Hovd & Skogestad, 1992, Skogestad & Havre, 1996. Other measures of interaction that exist in the literature are: the Block Relative Gain (Manousiouthakis et al, 1986;Grosdidier & Morari, 1987;Yu & Fan, 1990); the Relative Disturbance Gain (Stanley et al, 1985;Marino-Galarraga et al, 1985;Skogestad & Morari, 1987 a & b); or the Generalized Relative Disturbance Gain (Chang & Yu, 1992).…”

Section: Introductionmentioning

confidence: 99%