A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems

Litt, Jonathan S.; Simon, Donald L.; Garg, Sanjay; Guo, Ten-Heui; Mercer, Carolyn R.; Miller, R. V.; Behbahani, Alireza; Bahwa, Anupa; Jensen, Daniel T.

doi:10.2514/1.13048

Cited by 140 publications

(94 citation statements)

References 25 publications

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“…Fuzzy logic is one of the most effective approaches for intelligent control of complex nonlinear systems like aero-engines [4]. One of the important advantages of this approach is the simplicity of utilization.…”

Section: Introductionmentioning

confidence: 99%

“…With the integration of available models and algorithms for on-board operation and the resultant increase in on-board intelligence, engines of the future will be able to operate even more safely and reliably with reduced life cycle cost [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…While the traditional control techniques for aero-engines are time-tested and reliable, modern control techniques promise to provide improved control and therefore improved aircraft propulsion system performance [3,4].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…For future aerospace/aircraft propulsion systems to meet the requirements of decreased maintenance inspections, improved performance, and increased safety, the inclusion of intelligence into the propulsion system design and operation is necessary [1]- [3]. This implies the development of sensor systems able to operate at temperatures through 500 °C and higher to complement conditions present in an engine.…”

Section: Introductionmentioning

confidence: 99%

High Temperature Capacitive Pressure Sensor Employing a SiC Based Ring Oscillator

Meredith

Neudeck

Ponchak

et al. 2012

MSF

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In an effort to develop harsh environment electronic and sensor technologies for aircraft engine safety and monitoring, we have used capacitive-based pressure sensors to shift the frequency of a SiCelectronics-based oscillator to produce a pressure-indicating signal that can be readily transmitted, e.g. wirelessly, to a receiver located in a more benign environment. Our efforts target 500 °C, a temperature well above normal operating conditions of commercial circuits but within areas of interest in aerospace engines, deep mining applications and for future missions to the Venus atmosphere. This paper reports for the first time a ring oscillator circuit integrated with a capacitive pressure sensor, both operating at 500 °C. This demonstration represents a significant step towards a wireless pressure sensor that can operate at 500 °C and confirms the viability of 500 °C electronic sensor systems.

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“…This led to the application of many knowledge-based systems such as Fuzzy Logic (FL), neural networks (NN), genetic algorithms (GA) and probabilistic reasoning (PR) for the generation of complex models. Modern control techniques for full-sized jet engines may contain some of these sof t computing techniques to improve the propulsion system performance [10]. Some of these techniques have also been applied for the development of diagnostic and prognostic systems that can predict the progression of various faults to component failure [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Jet Engine Parameters for Control Design Using Genetic Programming

Arellano

Cant

Nolle

2014

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

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Abstract-The simulation of a jet engine behavior is widely used in many different aspects of the engine development and maintenance. Achieving high quality jet engine control systems requires the iterative use of these simulations to virtually test the performance of the engine avoiding any possible damage on the real engine. Jet engine simulations involve the use of mathematical models which are complex and may not always be available. This paper introduces an approach based on Genetic Programming (GP) to model different parameters of a small engine for control design such as the Exhaust Gas Temperature (EGT). The GP approach has no knowledge of the characteristics of the engine. Instead, the model is found by the evolution of models based on past measurements of parameters such as the pump voltage. Once the model is obtained, it is used to predict the behaviour of the jet engine one step ahead. The proposed approach is successfully applied for the simulation of a Behotec j66 jet engine and the results are presented.

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A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems

Cited by 140 publications

References 25 publications

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

High Temperature Capacitive Pressure Sensor Employing a SiC Based Ring Oscillator

Prediction of Jet Engine Parameters for Control Design Using Genetic Programming

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