Optimal multi-variable control of gas turbine engines

Lyantsev, O. D.; Breikin, Tim; Куликов, Г.Г.; Arkov, V.Y.

doi:10.1080/00207720310001657036

Cited by 12 publications

(17 citation statements)

References 5 publications

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“…Proportionalintegral based on pre-selection model is used to control the temperature of key components, as well as some simple proportional-integral control algorithms are still used in the acceleration control [6]. There are other advanced algorithms, such as adaptive PID [5], predictive control, open-loop optimal control [7], observer controller based on models and so on, which are applied to control the temperature and pressure of atmosphere inlet of compressor, pressure fluctuations before fuel control valve, and speed control. Certainly they can achieve certain effect with these control algorithms stated above.…”

Section: Current Status Of Gas Turbine Control Technologymentioning

confidence: 99%

Research on the application of model free adaptive (MFA) control in gas turbine

Zheng

Huang

2009

2009 9th International Conference on Electronic Measurement &Amp; Instruments

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In this paper, the current application status of gas turbine control technology is introduced, it presents the model free control theory and method in perspective of definition of model free control and model free adaptive control algorithms mainly existed and its advantages. This paper introduces some unsolved problems by using traditional control approaches. It gives reasons why the model free is suitable for the control of gas turbine. Afterwards, it describes how the model free adaptive control technology is applied to gas turbine and its related techniques in detail in this paper. In this part, it could provide theoretical foundation for applying model free control in actual gas turbine control system. Finally, conclusions and issues needed further research are put forward at the end of this paper.

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Section: Current Status Of Gas Turbine Control Technologymentioning

confidence: 99%

Research on the application of model free adaptive (MFA) control in gas turbine

Zheng

Huang

2009

2009 9th International Conference on Electronic Measurement &Amp; Instruments

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“…The main purpose of this section is to design a local optimal controller for the PCU (Lyantsev, et al, 2004) and to compare it with a genetically tuned PID controller. For simplicity, the pump subsystem is considered first.…”

Section: System Controlmentioning

confidence: 99%

“…12. The local optimal controller is designed and implemented as proposed in (Lyantsev, et al, 2004). For the pump model (SISO case) two states , are considered and the following equation is driven from the model in (2).…”

Section: Pump Controlmentioning

confidence: 99%

“…where h is a weighting coefficient indicating the level of uncertainty involved in the plant dynamics (Lyantsev, et al, 2004). From (7), u(i) can be obtained as a summation of the system input and output with different time delays and different coefficients.…”

Section: Pump Controlmentioning

confidence: 99%

“…Due to the increasing complexity of process control systems, multivariable process control has been of considerable attention over the past decades and numerous theoretical and practical works have been proposed in this area of research (Skogestad, and Postlethwaite, 2005). Among the existing methods of multivariable control, the Local Optimal Control (LOC) is a new method which was first introduced by Lyantsev, et al (2004). However, existing LOC approach is incapable of dealing with non-minimum phase systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control of Multivariable Systems Using Modified Local Optimal Controller

Mohamed

AbouZayed

Breikin

2008

IFAC Proceedings Volumes

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Active fault tolerant control based on a neuro fuzzy inference system applied to a two shafts gas turbine

Hadroug

Hafaifa

Batel

et al. 2018

Applied Artificial Intelligence

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The main aim of the present work is development of an active fault tolerant control for two shafts gas turbine fault detection and isolation based on a neuro fuzzy inference system adaptive approach. This approach combines the advantages of the neural networks with the fuzzy inference systems. The reconfiguration mechanism of the proposed active fault tolerant control is performed by detecting the malfunction of the studied gas turbine in an automatic manner. The obtained experimental results are presented to illustrate the great interest of the developed active fault tolerant control approach and to demonstrate its effectiveness in maintaining the stability with acceptable performance under the presence of defects in the presented gas turbine.

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Optimal multi-variable control of gas turbine engines

Cited by 12 publications

References 5 publications

Research on the application of model free adaptive (MFA) control in gas turbine

Research on the application of model free adaptive (MFA) control in gas turbine

Control of Multivariable Systems Using Modified Local Optimal Controller

Active fault tolerant control based on a neuro fuzzy inference system applied to a two shafts gas turbine

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