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• W2565016785 abstract "Original Research Paper Received 10 February 2016 Accepted 12 April 2016 Available Online 05 June 2016 In this paper, modeling of Min-Max controller and evolutionary multi-objective optimization for gain tuning controller of turbofan engine are presented. To achieve this purpose, first a turbofan engine is modeled in GSP software. Then engine parameters model, by using extracted GSP simulation data and based on NARX structure of neural network is developed. For model validation a test fuel signal is produced and model performance is assessed. Next, turbofan engines control requirements and constraints are described and a fuel controller based on Min-Max strategy is designed and diverse control loops in controller are described. Each of these loops has a proportional controller known as control gain of the min-max controller. For determining the gains of the controller, gain tuning process is formulated as a Genetic Algorithm Optimization problem in order for GA algorithm to find the best solution via its evolutionary generations. In this optimization problem, the settling time during acceleration and deceleration, engine fuel consumption and the amount of engine emissions are considered as objective functions to be minimized. The obtained results from simulation of optimized controller and engine show the final controller not only optimizes objective functions but also satisfies all control modes of engine during acceleration and deceleration modes." @default.
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• W2565016785 date "2016-01-01" @default.
• W2565016785 modified "2023-09-23" @default.
• W2565016785 title "MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZATION OF TURBOFAN ENGINE MIN-MAX FUEL CONTROL ALGORITHM" @default.
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