Home > Research > Publications & Outputs > Multivariable proportional-integral-plus (PIP) ...
View graph of relations

Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation. / Taylor, C. James; Shaban, E.
In: IEE Proceedings - Control Theory and Applications, Vol. 153, No. 3, 01.05.2006, p. 277-285.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Taylor, CJ & Shaban, E 2006, 'Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation', IEE Proceedings - Control Theory and Applications, vol. 153, no. 3, pp. 277-285. https://doi.org/10.1049/ip-cta:20050058

APA

Vancouver

Taylor CJ, Shaban E. Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation. IEE Proceedings - Control Theory and Applications. 2006 May 1;153(3):277-285. doi: 10.1049/ip-cta:20050058

Author

Taylor, C. James ; Shaban, E. / Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation. In: IEE Proceedings - Control Theory and Applications. 2006 ; Vol. 153, No. 3. pp. 277-285.

Bibtex

@article{9d6a41895d0b403fbf198474e3ef2857,
title = "Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation",
abstract = "Multivariable proportional-integral-plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner are represented in a non-minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm.",
author = "Taylor, {C. James} and E. Shaban",
year = "2006",
month = may,
day = "1",
doi = "10.1049/ip-cta:20050058",
language = "English",
volume = "153",
pages = "277--285",
journal = "IEE Proceedings - Control Theory and Applications",
issn = "1350-2379",
publisher = "Institute of Electrical Engineers",
number = "3",

}

RIS

TY - JOUR

T1 - Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation

AU - Taylor, C. James

AU - Shaban, E.

PY - 2006/5/1

Y1 - 2006/5/1

N2 - Multivariable proportional-integral-plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner are represented in a non-minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm.

AB - Multivariable proportional-integral-plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner are represented in a non-minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm.

U2 - 10.1049/ip-cta:20050058

DO - 10.1049/ip-cta:20050058

M3 - Journal article

VL - 153

SP - 277

EP - 285

JO - IEE Proceedings - Control Theory and Applications

JF - IEE Proceedings - Control Theory and Applications

SN - 1350-2379

IS - 3

ER -