Fuzzy scheduling control of a gas turbine aero-engine: a multiobjective approach

Chipperfield, A.J.; Bica, B.; Fleming, Peter J.

doi:10.1109/tie.2002.1005378

Cited by 75 publications

(42 citation statements)

References 21 publications

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“…It has been widely employed in control problems, particularly due to its ability to mimic the behavior of nonlinear plants. By ensuring that a properly formulated rule base is found, a fuzzy system can provide smooth transitions between operating regimes [12,22]. An FLC utilizes fuzzy logic to convert linguistic information based on expert knowledge into an automatic control strategy.…”

Section: Designing a Fuel Flow Controller By The Aid Of Fuzzy Logic Mmentioning

confidence: 99%

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Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Bazazzadeh

Badihi

Shahriari

2011

International Journal of Aerospace Engineering

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This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC) for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc.) are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

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Section: Designing a Fuel Flow Controller By The Aid Of Fuzzy Logic Mmentioning

confidence: 99%

“…In [12], the authors studied a combination of two potential techniques; fuzzy logic and evolutionary algorithms for a specific gas turbine. The controlling parameters include Inlet Guide Vane (IGV) angle and nozzle area.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Bazazzadeh

Badihi

Shahriari

2011

International Journal of Aerospace Engineering

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“…Suitable AI controllers can be employed to address operating context changes given specified safety objectives. For example, previous work [7] has demonstrated the use of fuzzy logic systems for control of Inlet Guide Vanes, fuel flow (WFE), and engine nozzle (NOZZ) using Mamdani and Takagi-Sugeno [8] fuzzy rules. Such work has been shown to offer improved performance (such as thrust maximisation) over linear or non-linear polynomial schedulers [7].…”

Section: Problem Definition: Managing Changing Requirementsmentioning

confidence: 99%

“…As highlighted in [18] this greatly limits the potential for acceptable risk reduction strategies by focussing on a limited and potentially inadequate solution space. Other work on the use of Evolutionary algorithms for devising optimal Engine schedulers include the Multi-Objective Genetic Algorithm (MOGA) [7]. As described in [7] MOGA has been shown to be a competent algorithm for finding optimal fuzzy schedulers for GTE control.…”

Section: Component (Controller) Solution Generatormentioning

confidence: 99%

“…Other work on the use of Evolutionary algorithms for devising optimal Engine schedulers include the Multi-Objective Genetic Algorithm (MOGA) [7]. As described in [7] MOGA has been shown to be a competent algorithm for finding optimal fuzzy schedulers for GTE control. MOGA is composed of three levels and uses genetic algorithms to search for an optimal controller solution of fuzzy control rules.…”

Section: Component (Controller) Solution Generatormentioning

confidence: 99%

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Establishing a Framework for Dynamic Risk Management in ‘Intelligent’ Aero-Engine Control

Kurd

Kelly

McDermid

et al. 2009

Lecture Notes in Computer Science

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Abstract. The behaviour of control functions in safety critical software systems is typically bounded to prevent the occurrence of known system level hazards. These bounds are typically derived through safety analyses and can be implemented through the use of necessary design features. However, the unpredictability of real world problems can result in changes in the operating context that may invalidate the behavioural bounds themselves, for example, unexpected hazardous operating contexts as a result of failures or degradation. For highly complex problems it may be infeasible to determine the precise desired behavioural bounds of a function that addresses or minimises risk for hazardous operation cases prior to deployment. This paper presents an overview of the safety challenges associated with such a problem and how such problems might be addressed. A self-management framework is proposed that performs on-line risk management. The features of the framework are shown in context of employing intelligent adaptive controllers operating within complex and highly dynamic problem domains such as Gas-Turbine Aero Engine control. Safety assurance arguments enabled by the framework necessary for certification are also outlined.

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Nonlinear reference tracking control of a gas turbine with load torque estimation

Pongrácz

Ailer²,

Hangos

et al. 2007

Adaptive Control & Signal

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Input-output linearization-based adaptive reference tracking control of a low-power gas turbine model is presented in this paper. The gas turbine is described by a third-order nonlinear input-affine state-space model, where the manipulable input is the fuel mass flowrate and the controlled output is the rotational speed. The stability of the one-dimensional zero dynamics of the controlled plant is investigated via phase diagrams. The input-output linearizing feedback is extended with a load torque estimator algorithm resulting in an adaptive feedback scheme. The tuning of controller parameters is performed considering three main design goals: appropriate settling time, robustness against environmental disturbances and model parameter uncertainties, and avoiding the saturation of the actuator. Simulations show that the closed-loop system is robust with respect to the variations in uncertain model and environmental parameters and its performance satisfies the defined requirements.

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Fuzzy scheduling control of a gas turbine aero-engine: a multiobjective approach

Cited by 75 publications

References 21 publications

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Establishing a Framework for Dynamic Risk Management in ‘Intelligent’ Aero-Engine Control

Nonlinear reference tracking control of a gas turbine with load torque estimation

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