Tuning of fuzzy fuel controller for aero-engine thrust regulation and safety considerations using genetic algorithm

Montazeri-Gh, Morteza; Safari, Abdolreza

doi:10.1016/j.ast.2010.10.004

Cited by 26 publications

(18 citation statements)

References 17 publications

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“…The use of soft computing, because of the simplicity and acceptable performance in turbine control areas is usually interesting for researchers in a long time ago [3][4][5][6][7][8][9][10][11][12]. It is to note that fuzzy-based techniques are generally taken into consideration as the most important methods in the area of gas turbine control and also in its launch process that have been particularly attended [13][14][15][16][17][18]. In a word, there are some potential works in this field of gas turbine speed control as well as the study of the frequency variations in line with the amount of power produced, while the performance of classical control approaches are considered.…”

Section: The Related Workmentioning

confidence: 99%

Controlling disturbances of islanding in a gas power plant via fuzzy-based neural network approach with a focus on load-shedding system

Moloudi

Mazinan

2018

Complex Intell. Syst.

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In a number of power plants such as the South Pars Gas Company, efficient control performance regarding the turbine generator is always needed to avoid facing disturbances of islanding in the supply of power, at each instance of time. It is particularly important to deal with through state-of-the-art techniques, due to the fact that the separation of the electrical grid may increase or reduce the speed of the above-referenced turbine generator and correspondingly the frequency, in its permitted range, may be deviated to stop power generation, abruptly. With this key goal, a set of control strategies through the representation of the gas turbine via Rowen is proposed in the present research. In a word, there are some classical and intelligence-based control approaches to cope with the disturbance of islanding with a focus on load-shedding system. In reality, the proposed investigation is mainly concentrating on the fuzzy-based neural network control approach to cope with the turbulence of frequency deviation. The main contribution made in this research is to deal with gas turbine model in association with generator in conjunction with load-shedding system, while intelligence-based techniques considering fuzzy, neural network and genetic algorithm are synchronously established to design the control strategy with high accuracy. In the sequel, the effectiveness of the proposed intelligence-based control approach is verified, while the classical proportionalintegral-derivative control approach via the Ziegler-Nichols and also the simulated annealing tuning processes are taken as the benchmarks.

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Section: The Related Workmentioning

confidence: 99%

Controlling disturbances of islanding in a gas power plant via fuzzy-based neural network approach with a focus on load-shedding system

Moloudi

Mazinan

2018

Complex Intell. Syst.

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“…Progressive methodologies like robust control [30], linear quadratic control [31,32], and model predictive and fuzzy control [33,34] are able to produce controllers robust enough to cover a large spectrum of states and uncertainties; they are however often computationally too complex as shown in the references. Another approach, which has already been often used in solution of control problems, is to design simpler specific controllers for specific operational states of the investigated dynamic system for example in an application using different controllers for gas turbine generator and power system of aeropropulsion system as described in [38].…”

Section: Situational Control Methodology Framework Designmentioning

confidence: 99%

“…They were successfully applied in the envisioned engine control systems, the robust control algorithm being described in [30], and LQ control algorithms applied in gas turbine engine control [31,32]. Even more advanced methodologies like model predictive control [33] or hybrid fuzzy-genetic algorithms have been proposed in adaptation of gas turbine engine controllers as described in [34].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of them base their results and conclusions solely on simulations like [19,27,30,34] or present only conceptual designs like [21,22,25]. Application of methodologies from the field of computational intelligence can also be seen together with interconnection of diagnostic and control systems [21,22,27], which can further enhance the control system's reliability and efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Situational Control of Small Turbojet Engines

Andoga

Főző

Judicak

et al. 2018

International Journal of Aerospace Engineering

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Improvements in reliability, safety, and operational efficiency of aeroengines can be brought in a cost-effective way using advanced control concepts, thus requiring only software updates of their digital control systems. The article presents a comprehensive approach in modular control system design suitable for small gas turbine engines. The control system is based on the methodology of situational control; this means control of the engine under all operational situations including atypical ones, also integrating a diagnostic system, which is usually a separate module. The resulting concept has been evaluated in real-world laboratory conditions using a unique design of small turbojet engine iSTC-21v as well as a state-of-the-art small turbojet engine TJ-100. Our results show that such advanced control system can bring operational quality of an engine with old turbocompressor core iSTC-21v on par with state-of-the-art engines. Dedicated to the memory of Ladislav Madarász

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“…The fuzzy decision makers have to choose the best configuration of performance indices coefficients. Also, this configuration balanced performance indices intelligently based on decision rules by mission designer in user-friendly manner [15][16][17][18][19][20]. Moreover, other intelligent tool is considered as GA-PSO optimization loop to demonstrate this novel method.…”

Section: Introductionmentioning

confidence: 98%

Design of the LQR controller and observer with fuzzy logic GA and GA-PSO algorithm for triple an inverted pendulum and cart system

Molazadeh

Banazadeh

Shafieenejad

2014

Proceedings of the 2014 International Conference on Advanced Mechatronic Systems

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In this paper, designing of the LQR controller and observer with intelligent tools for the triple inverted pendulum is investigated. Intelligent tools are considered as GA and GA-PSO optimization algorithms and fuzzy logic to qualify achieving LQR gains. The pendulum is swung up from the vertical position to the unstable position. The rules for the controlled swing up are heuristically achieved such that each swing results are controlled. The inverted pendulum and cart system are modeled and constructed their equation based on energy method. Also, the performances of the proposed fuzzy logic controller, GA and GA-PSO tuning LQR gains are investigated and compared. Simulation results show that the fuzzy Logic Controllers are far more superior in terms of overshoot, settling time and response to parameter changes.

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Tuning of fuzzy fuel controller for aero-engine thrust regulation and safety considerations using genetic algorithm

Cited by 26 publications

References 17 publications

Controlling disturbances of islanding in a gas power plant via fuzzy-based neural network approach with a focus on load-shedding system

Controlling disturbances of islanding in a gas power plant via fuzzy-based neural network approach with a focus on load-shedding system

Intelligent Situational Control of Small Turbojet Engines

Design of the LQR controller and observer with fuzzy logic GA and GA-PSO algorithm for triple an inverted pendulum and cart system

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