Relevant bibliographies by topics / Turbojet, Modeling, Control, Fuzzy Logic

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Turbojet, Modeling, Control, Fuzzy Logic'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Turbojet, Modeling, Control, Fuzzy Logic"

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Whalen, Thomas, Brian Schott, and Gwangyong Gim. "Control of error in fuzzy logic modeling." Fuzzy Sets and Systems 80, no. 1 (May 1996): 23–35. http://dx.doi.org/10.1016/0165-0114(95)00280-4.

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Pedrycz, Witold. "Logic-driven fuzzy modeling with fuzzy multiplexers." Engineering Applications of Artificial Intelligence 17, no. 4 (June 2004): 383–91. http://dx.doi.org/10.1016/j.engappai.2004.04.011.

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Vachtsevanos, George. "Large-scale systems: modeling, control, and fuzzy logic." Automatica 37, no. 9 (September 2001): 1500–1502. http://dx.doi.org/10.1016/s0005-1098(01)00108-x.

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Ghaemi, Sehraneh, Sohrab Khanmohammadi, and Mohammadali Tinati. "Driver's Behavior Modeling Using Fuzzy Logic." Mathematical Problems in Engineering 2010 (2010): 1–29. http://dx.doi.org/10.1155/2010/172878.

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In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model calledModel Iis presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules calledModel IIandModel IIIby using Sugeno fuzzy inference.Model IIandModel IIIhave less linguistic terms thanModel Ifor the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.
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Cigánek, Ján, Filip Noge, and Štefan Kozák. "Modeling and Control of Mechatronic Systems Using Fuzzy Logic." International Review of Automatic Control (IREACO) 7, no. 1 (January 31, 2014): 45. http://dx.doi.org/10.15866/ireaco.v7i1.1291.

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Toumodge, S. "Large-Scale Systems: Modeling, Control, and Fuzzy Logic [Bookshelf]." IEEE Control Systems 18, no. 3 (June 1998): 84. http://dx.doi.org/10.1109/mcs.1998.687623.

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BESSAAD, Taieb. "Modeling and Control of multimachines System Using Fuzzy Logic." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 5 (May 5, 2019): 143–48. http://dx.doi.org/10.15199/48.2019.05.34.

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Zhi Liu and Han-Xiong Li. "A probabilistic fuzzy logic system for modeling and control." IEEE Transactions on Fuzzy Systems 13, no. 6 (December 2005): 848–59. http://dx.doi.org/10.1109/tfuzz.2005.859326.

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Karthikeyan, R., R. K. Ganesh Ram, and V. Kalaichelvi. "Modeling and Control Techniques for Microstructure Development." Applied Mechanics and Materials 541-542 (March 2014): 317–23. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.317.

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True stress-strain data is obtained for 6061Al/ 10% SiC composites by hot compression test. Mathematical models for % volume of recrystallization and diameter of the recrystallized grains are developed with process parameters such as strain, strain rate and temperature. These models are applied for optimization of the grain size and % volume of recrystallization. An attempt has been made to control microstructure evolution during hot deformation using fuzzy logic controller through simulation in MATLAB software. The fuzzy logic controller parameters are tuned using genetic algorithm.
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Jomaa, M., M. Abbes, F. Tadeo, and A. Mami. "Greenhouse Modeling, Validation and Climate Control based on Fuzzy Logic." Engineering, Technology & Applied Science Research 9, no. 4 (August 10, 2019): 4405–10. http://dx.doi.org/10.48084/etasr.2871.

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This paper deals with the modeling and control of the air temperature and humidity in greenhouses. A physical model of the greenhouse used in the Simulink/Matlab environment is elaborated to simulate both temperature and indoor humidity. As a solution to the non-linearity and complexity of the greenhouse system, a fuzzy logic method is developed to control the actuators that are installed inside the greenhouse for heating, ventilation, humidification and cooling to obtain a suitable microclimate.
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Dissertations / Theses on the topic "Turbojet, Modeling, Control, Fuzzy Logic"

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Polat, Cuma. "An Electronic Control Unit Design For A Miniature Jet Engine." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611442/index.pdf.

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Gas turbines are widely used as power sources in many industrial and transportation applications. This kind of engine is the most preferred prime movers in aircrafts, power plants and some marine vehicles. They have different configurations according to their mechanical constructions such as turbo-prop, turbo-shaft, turbojet, etc. These engines have different efficiencies and specifications and some advantages and disadvantages compared to Otto-Cycle engines. In this thesis, a small turbojet engine is investigated in order to find different control algorithms. AMT Olympus HP small turbojet engine has been used to determine the mathematical model of a gas turbine engine. Some important experimental data were taken from AMT Olympus engine by making many experiments. All components of the engine have been modeled by using laws of thermodynamics and some arithmetic calculations such as numerical solution of nonlinear differential equations, digitizing compressor and turbine map etc. This mathematical model is employed to create control algorithm of the engine. At first, standard control strategies had been considered such as P (proportional), PI (proportional integral), and PID (proportional-integral-differential) controllers. Because of the nonlinearities in gas turbines, standard control algorithms are not commonly used in literature. At the second stage fuzzy logic controllers have been designed to control the engine efficiently. This control algorithm was combined with mathematical of the engine in MATLAB environment and input-output relations were investigated. Finally, fuzzy logic control algorithm was embedded into an electronic controller.
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Antão, Rómulo José Magalhães Martins. "Type-2 fuzzy logic: uncertain systems' modeling and control." Doctoral thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/18041.

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Doutoramento em Engenharia Eletrotécnica
A última fronteira da Inteligência Artificial será o desenvolvimento de um sistema computacional autónomo capaz de "rivalizar" com a capacidade de aprendizagem e de entendimento humana. Ainda que tal objetivo não tenha sido até hoje atingido, da sua demanda resultam importantes contribuições para o estado-da-arte tecnológico atual. A Lógica Difusa é uma delas que, influenciada pelos princípios fundamentais da lógica proposicional do raciocínio humano, está na base de alguns dos sistemas computacionais "inteligentes" mais usados da atualidade. A teoria da Lógica Difusa é uma ferramenta fundamental na suplantação de algumas das limitações inerentes à representação de informação incerta em sistemas computacionais. No entanto esta apresenta ainda algumas lacunas, pelo que diversos melhoramentos à teoria original têm sido introduzidos ao longo dos anos, sendo a Lógica Difusa de Tipo-2 uma das mais recentes propostas. Os novos graus de liberdade introduzidos por esta teoria têm-se demonstrado vantajosos, particularmente em aplicações de modelação de sistemas não-lineares complexos. Uma das principais vantagens prende-se com o aumento da robustez dos modelos assim desenvolvidos comparativamente àqueles baseados nos princípios da Lógica Difusa de Tipo-1 sem implicar necessariamente um aumento da sua dimensão. Tal propriedade é particularmente vantajosa considerando que muitas vezes estes modelos são utilizados como suporte ao desenvolvimento de sistemas de controlo que deverão ser capazes de assegurar o comportamento ótimo de um processo em condições de operação variáveis. No entanto, o estado-da-arte da teoria de controlo de sistemas baseada em modelos não tem integrado todos os melhoramentos proporcionados pelo desenvolvimento de modelos baseados nos princípios da Lógica Difusa de Tipo-2. Por essa razão, a presente tese propõe-se a abordar este tópico desenvolvendo uma metodologia de síntese de Controladores Preditivos baseados em modelos Takagi-Sugeno seguindo os princípios da Lógica Difusa de Tipo-2. De modo a cumprir este objetivo, quatro linhas de investigação serão debatidas neste trabalho.Primeiramente proceder-se-á ao desenvolvimento de uma metodologia de treino de Modelos Difusos de Tipo-2 simplificada, focada em dois paradigmas: manter a clareza dos intervalos de incerteza introduzidos sobre um Modelo Difuso de Tipo-1; assegurar a validade dos diversos modelos localmente lineares que constituem a estrutura Takagi- Sugeno, de modo a torná-los adequados a métodos de síntese de controladores baseados em modelos. O modelo desenvolvido é tipicamente utilizado para extrapolar o comportamento do sistema numa janela temporal futura. No entanto, quando usados em aproximações de sistemas não lineares, os modelos do tipo Takagi-Sugeno estabelecem um compromisso entre exatidão e complexidade computacional. Assim, é proposta a utilização dos princípios da Lógica Difusa de Tipo-2 para reduzir a influência dos erros de modelação nas estimações obtidas através do ajuste dos intervalos de incerteza dos parâmetros do modelo. Com base na estrutura Takagi-Sugeno, um método de linearização local de modelos não-lineares será utilizado em cada ponto de funcionamento do sistema de modo a obter os parâmetros necessários para a síntese de um controlador otimizado numa janela temporal futura de acordo com os princípios da teoria de Controlo Preditivo Generalizado - um dos algoritmos de Controlo Preditivo mais utilizado na indústria. A qualidade da resposta do sistema em malha fechada e a sua robustez a perturbações serão então comparadas com implementações do mesmo algoritmo baseadas em métodos de modelação mais simples. Para concluir, o controlador proposto será implementado num System-on-Chip baseado no core ARM Cortex-M4. Com o propósito de facilitar a realização de testes de implementação de algoritmos de controlo em sistemas embutidos, será apresentada também uma plataforma baseada numa arquitetura Processor-In-the-Loop, que permitirá avaliar a execução do algoritmo proposto em sistemas computacionais com recursos limitados, aferindo a existência de possíveis limitações antes da sua aplicação em cenários reais. A validade do novo método proposto é avaliada em dois cenários de simulação comummente utilizados em testes de sistemas de controlo não-lineares: no Controlo da Temperatura de uma Cuba de Fermentação e no Controlo do Nível de Líquidos num Sistema de Tanques Acoplados. É demonstrado que o algoritmo de controlo desenvolvido permite uma melhoria da performance dos processos supramencionados, particularmente em casos de mudança rápida dos regimes de funcionamento e na presença de perturbações ao processo não medidas.
The development of an autonomous system capable of matching human knowledge and learning capabilities embedded in a compact yet transparent way has been one of the most sought milestones of Artificial Intelligence since the invention of the first mechanical general purpose computers. Such accomplishment is yet to come but, in its pursuit, important contributions to the state-of-the-art of current technology have been made. Fuzzy Logic is one of such, supporting some of the most used frameworks for embedding human-like knowledge in computational systems. The theory of Fuzzy Logic overcame some of the difficulties that the inherent uncertainty in information representations poses to the development of computational systems. However, it does present some limitations so, aiming to further extend its capabilities, several improvements over its original formalization have been proposed over the years such as Type-2 Fuzzy Logic - one of its most recent advances. The additional degrees of freedom of Type-2 Fuzzy Logic are showing greater potential to supplant its original counterpart, especially in complex non-linear modeling tasks. One of its main outcomes is its capability of improving the developed model’s robustness without necessarily increasing its dimensionality comparatively to a Type-1 Fuzzy Model counterpart. Such feature is particularly advantageous if one considers these model as a support for developing control systems capable of maintaining a process’s optimal performance over changing operating conditions. However, state-of-the art model-based control theory does not seem to be taking full advantage of the improvements achieved with the development of Type-2 Fuzzy Logic based models. Therefore, this thesis proposes to address this problem by developing a Model Predictive Control system supported by Interval Type-2 Takagi- Sugeno Fuzzy Models. To accomplish this goal, four main research directions are covered in this work.Firstly, a simpler method for training a Type-2 Takagi-Sugeno Fuzzy Model focused on two main paradigms is proposed: maintaining a meaningful interpretation of the uncertainty intervals embedded over an estimated Type-1 Fuzzy Model; ensuring the validity of several locally linear models that constitute the Takagi-Sugeno structure in order to make them suitable for model-based control approaches. Based on the developed model, a multi-step ahead estimation of the process behavior is extrapolated. However, as Takagi-Sugeno Fuzzy Models establish a trade-off between accuracy and computational complexity when used as a non-linear process approximation, it is proposed to apply the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on the obtained estimations by adjusting the model parameters’ uncertainty intervals. Supported by the developed Type-2 Takagi-Sugeno Fuzzy Model, a locally linear approximation of each current operation point is used to obtain the optimal control law over a prediction horizon according to the principles of Generalized Predictive Control - one of the most used Model Predictive Control algorithms in Industry. The improvements in terms of closed loop tracking performance and robustness to unmodeled operation conditions are then assessed comparatively to Generalized Predictive Control implementations based on simpler modeling approaches. Ultimately, the proposed control system is implemented in a general purpose System-on-a-Chip based on a ARM Cortex-M4 core. A Processor-In-the-Loop testing framework, developed to support the implementation of control loops in embedded systems, is used to evaluate the algorithm’s turnaround time when executed in such computationally constrained platform, assessing its possible limitations before deployment in real application scenarios. The applicability of the new methods introduced in this thesis is illustrated in two simulated processes commonly used in non-linear control benchmarking: the Temperature Control of a Fermentation Reactor and the Liquid Level Control of a Coupled Tanks System. It is shown that the developed control system achieves an improved closed loop performance of the above mentioned processes, particularly in the cases of quick changes in the operation regime and in presence of unmeasured external disturbances.
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Soufian, Majeed. "Hard and soft computing techniques for non-linear modeling and control with industrial applications." Thesis, Manchester Metropolitan University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273053.

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Emami, Mohammad Reza. "Systematic methodology of fuzzy-logic modeling and control and application to robotics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ28276.pdf.

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Shook, David Adam. "Control of a benchmark structure using GA-optimized fuzzy logic control." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1088.

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Wijayasekara, Dumidu S. "IMPROVING UNDERSTANDABILITY AND UNCERTAINTY MODELING OF DATA USING FUZZY LOGIC SYSTEMS." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4126.

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The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty. Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague terms that rely on human understandable terms. The use of linguistic terms and simple consequential rules increase the understandability of system behavior as well as data. Use of vague terms and modeling data from non-discrete prototypes enables modeling of uncertainty. However, due to recent trends, the primary research of fuzzy logic have been diverged from the basic concept of understandability. Furthermore, high computational costs to achieve robust uncertainty modeling have led to restricted use of such fuzzy systems in real-world applications. Thus, the goal of this dissertation is to present algorithms and techniques that improve understandability and uncertainty modeling using Fuzzy Logic Systems. In order to achieve this goal, this dissertation presents the following major contributions: 1) a novel methodology for generating Fuzzy Membership Functions based on understandability, 2) Linguistic Summarization of data using if-then type consequential rules, and 3) novel Shadowed Type-2 Fuzzy Logic Systems for uncertainty modeling. Finally, these presented techniques are applied to real world systems and data to exemplify their relevance and usage.
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Soderstrom, David. "Fuzzy logic modeling and intelligent sliding mode control techniques for the individualization of theophylline therapy to pediatric patients." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/19097.

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Mok, Tsz-kin, and 莫子建. "Modeling, analysis and control design for the UPFC with fuzzy theory and genetic algorithm application." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31224969.

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Arsava, Kemal Sarp. "Modeling, Control and Monitoring of Smart Structures under High Impact Loads." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/105.

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In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a different vessel, or if it were located elsewhere with different in-situ conditions. With these limitations in mind, we propose different solutions that use smart control technology to mitigate impact hazard on structures. However, it is challenging to develop an accurate mathematical model of the integrated structure-smart control systems. The reason is due to the complicated nonlinear behavior of the integrated nonlinear systems and uncertainties of high impact forces. In this context, novel algorithms are developed for identification, control and monitoring of nonlinear responses of smart structures under high impact forces. To evaluate the proposed approaches, a smart aluminum and two smart reinforced concrete beam structures were designed, manufactured, and tested in the High Impact Engineering Laboratory of Civil and Environmental Engineering at WPI. High-speed impact force and structural responses such as strain, deflection and acceleration were measured in the experimental tests. It has been demonstrated from the analytical and experimental study that: 1) the proposed system identification model predicts nonlinear behavior of smart structures under a variety of high impact forces, 2) the developed structural health monitoring algorithm is effective in identifying damage in time-varying nonlinear dynamic systems under ambient excitations, and 3) the proposed controller is effective in mitigating high impact responses of the smart structures.
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Tugsal, Umut. "FAULT DIAGNOSIS OF ELECTRONIC FUEL CONTROL (EFC) VALVES VIA DYNAMIC PERFORMANCE TEST METHOD." ProQuest, 2009. http://hdl.handle.net/1805/2094.

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Indiana University-Purdue University Indianapolis (IUPUI)
Electronic Fuel Control (EFC) valve regulates fuel flow to the injector fuel supply line in the Cummins Pressure Time (PT) fuel system. The EFC system controls the fuel flow by means of a variable orifice that is electrically actuated. The supplier of the EFC valves inspects all parts before they are sent out. Their inspection test results provide a characteristic curve which shows the relationship between pressure and current provided to the EFC valve. This curve documents the steady state characteristics of the valve but does not adequately capture its dynamic response. A dynamic test procedure is developed in order to evaluate the performance of the EFC valves. The test itself helps to understand the effects that proposed design changes will have on the stability of the overall engine system. A by product of this test is the ability to evaluate returned EFC valves that have experienced stability issues. The test determines whether an EFC valve is faulted or not before it goes out to prime time use. The characteristics of a good valve and bad valve can be observed after the dynamic test. In this thesis, a mathematical model has been combined with experimental research to investigate and understand the behavior of the characteristics of different types of EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. System Identification has been addressed to determine the transfer functions of the different types of EFC valves that were experimented. Methods have been used both in frequency domain as well as time domain. Also, based on the characteristic patterns exhibited by the EFC valves, fuzzy logic has been implemented for the use of pattern classification.
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Books on the topic "Turbojet, Modeling, Control, Fuzzy Logic"

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Nguyen, Hung T. Fuzzy Systems: Modeling and Control. Boston, MA: Springer US, 1998.

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Jamshidi, Mohammad. Large-scale systems: Modeling, control, and fuzzy logic. Upper Saddle River, NJ: Prentice Hall, 1997.

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1944-, Nguyen Hung T., and Prasad Nadipuram R, eds. Fuzzy modeling and control: Selected works of M. Sugeno. Boca Raton: CRC Press, 1999.

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Robyns, Benoit. Vector Control of Induction Machines: Desensitisation and Optimisation Through Fuzzy Logic. London: Springer London, 2012.

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Tucci, Mario, and Marco Garetti, eds. Proceedings of the third International Workshop of the IFIP WG5.7. Florence: Firenze University Press, 2002. http://dx.doi.org/10.36253/88-8453-042-3.

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Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment.
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Jamshidi, Mohammad. Large-Scale Systems: Modeling, Control and Fuzzy Logic. Prentice Hall, 1996.

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Large-Scale Systems: Modeling, Control and Fuzzy Logic. Prentice Hall, 1996.

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Martins, Rui, Rómulo Antão, Alexandre Mota, and José Tenreiro Machado. Type-2 Fuzzy Logic: Uncertain Systems’ Modeling and Control. Springer, 2017.

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Martins, Rui, Rómulo Antão, Alexandre Mota, and José Tenreiro Machado. Type-2 Fuzzy Logic: Uncertain Systems’ Modeling and Control. Springer, 2018.

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Martins, Rui, Rómulo Antão, and Alexandre Mota. Type-2 Fuzzy Logic: Uncertain Systems' Modeling and Control. Springer, 2017.

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Book chapters on the topic "Turbojet, Modeling, Control, Fuzzy Logic"

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Al-Khalidy, Mohammed Majid Mohammed, and Fatma Abdulnabi Al-attar. "Step by Step Modeling and Tuning for Fuzzy Logic Controller." In Informatics in Control, Automation and Robotics, 81–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25899-2_12.

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Valera, Leobardo, Angel Garcia Contreras, and Martine Ceberio. "“On-the-fly” Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling." In Fuzzy Logic in Intelligent System Design, 293–99. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67137-6_33.

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Reel, Smarti, and Ashok Kumar Goel. "Artificial Neural Networks and Fuzzy Logic in Process Modeling and Control." In Communications in Computer and Information Science, 808–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25734-6_144.

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Joo, Y. H., H. S. Hwang, K. B. Woo, and K. B. Kim. "Fuzzy System Modeling and its Application to Mobile Robot Control." In Fuzzy Logic and its Applications to Engineering, Information Sciences, and Intelligent Systems, 147–56. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-009-0125-4_14.

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Cazarez-Castro, Nohe R., Luis T. Aguilar, Oscar Castillo, and Antonio Rodríguez-Dŕaz. "Controlling Unstable Non-Minimum-Phase Systems with Fuzzy Logic: The Perturbed Case." In Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, 245–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04514-1_14.

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Martinez, Ricardo, Oscar Castillo, Luis T. Aguilar, and Antonio Rodriguez. "Evolutionary Optimization of Type-2 Fuzzy Logic Systems Applied to Linear Plants." In Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, 17–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04514-1_2.

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Vigneysh, T., and N. Kumarappan. "Dynamic Modeling and Control of Utility-Interactive Microgrid Using Fuzzy Logic Controller." In Lecture Notes in Electrical Engineering, 97–106. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4852-4_9.

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Mendez, Gerardo M., and Ma De Los Angeles Hernandez. "Hybrid Interval Type-1 Non-singleton Type-2 Fuzzy Logic Systems Are Type-2 Adaptive Neuro-fuzzy Inference Systems." In Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, 53–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04514-1_4.

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Ciabattoni, Lucio, Massimo Grisostomi, Gianluca Ippoliti, and Sauro Longhi. "Household Electrical Consumptions Modeling and Management Through Neural Networks and Fuzzy Logic Approaches." In Complex System Modelling and Control Through Intelligent Soft Computations, 437–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12883-2_16.

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Solano-Aragón, Cinthya, and Arnulfo Alanis. "Multi-Agent System with Fuzzy Logic Control for Autonomous Mobile Robots in Known Environments." In Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, 33–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04514-1_3.

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Conference papers on the topic "Turbojet, Modeling, Control, Fuzzy Logic"

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Toprak, Suha, Aydan Erkmen, and I. Akmandor. "Identification and control of a radial turbojet with neural network and fuzzy logic." In 36th AIAA Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-1016.

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Amirante, Riccardo, Luciano Andrea Catalano, and Paolo Tamburrano. "Thrust Control of Small Turbojet Engines Using Fuzzy Logic: Design and Experimental Validation." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68892.

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The aim of this paper is to propose an effective technique which employs a proportional-integral Fuzzy logic controller for the thrust regulation of small scale turbojet engines, capable of ensuring high performance in terms of response speed, precision and stability. Fuzzy rules have been chosen by logical deduction and some specific parameters of the closed loop control have been optimized using a numerical simulator, so as to achieve rapidity and stability of response, as well as absence of overshoots. The proposed Fuzzy logic controller has been tested on the Pegasus MK3 microturbine: the high response speed and precision of the proposed thrust control, revealed by the simulations, have been confirmed by several experimental tests with step response. Its stability has been demonstrated by means of the frequency response analysis of the system. The proposed thrust control technique has general validity and can be applied to any small-scale turbojet engine, as well as to microturbines for electricity production, provided that thrust being substituted with the net mechanical power.
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Amirante, Riccardo, Luciano Andrea Catalano, and Paolo Tamburrano. "An Adaptive Fuzzy Logic Algorithm for the Thrust Control of a Small Turbojet Engine." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22510.

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This paper proposes a Fuzzy technique for the thrust control of small-scale turbo-jet engines, as an effective alternative to conventional PID techniques. Fuzzy rules have been preliminarly chosen and tuned so as to achieve rapidity and stability of response, as well as absence of overshoots, by simulating the transient operation of the Pegasus MK3 small-scale turbo-jet. Three experimental tests with large increases or decreases of set thrust have been carried out on the same engine: excellent results in terms of response speed, stability and absence of overshoots have been achieved. The proposed thrust control technique has general validity and can be applied to any small-scale turbojet engine, as well as to microturbines for electricity production, provided that thrust being substituted with the net mechanical power.
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Zalapa, Salvador Alvarez, and Roberto Tapia Sanchez. "Exoskeleton robot modeling and Fuzzy Logic Control." In 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2016. http://dx.doi.org/10.1109/ropec.2016.7830604.

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Üşenmez, Serdar, Sinan Ekinci, Oğuz Uzol, and İlkay Yavrucuk. "Application of a Fuzzy Logic Controller for Speed Control on a Small-Scale Turbojet Engine." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27158.

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Having a small-scale turbojet engine operate at a desired speed with minimum steady state error, while maintaining good transient response is crucial in many applications, such as UAVs, and requires precise control of the fuel flow. In this paper, first the mathematical model of a Small-Scale Turbojet Engine (SSTE) is obtained using system identification tests, and then based on this model, a classical PI controller is designed. Afterwards, to improve on the transient response and steady state performance of this classical controller, a Fuzzy Logic Controller (FLC) is designed. The design process for the FLC employs logical deduction based on knowledge of the engine behavior and iterative tuning in the light of software- and hardware-in-the-loop simulations. The classical and fuzzy logic controllers are both implemented on an in-house, embedded Electronic Control Unit (ECU) running in real time. This ECU is an integrated device carrying a microcontroller based board, a fuel pump, fuel line valves, speed sensor and exhaust gas temperature sensor inputs, and starter motor and glow plug driver outputs. It mainly functions by receiving a speed reference value via its serial communication interface. Based on this reference, a voltage is calculated and applied to the fuel pump in order to regulate the fuel flow into the engine, thereby bringing the engine speed to the desired value. Pre-defined procedures for starting and stopping the engine are also automatically performed by the ECU. Further, it connects to a computer running an in-house comprehensive Graphical User Interface (GUI) software for operating, monitoring, configuration and diagnostics purposes. The designed controllers are used to drive a generic SSTE. Reference inputs consisting of step, ramp and chirp profiles are applied to the controllers. The engine response using both controllers are recorded and inspected. The results show that the FLC exhibits a comparable performance to the classical controller, with possible opportunities to improve this performance.
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Lin, Yueh-Jaw, and Tian-Soon Lee. "Modeling for Fuzzy Logic Control of Deformable Manipulators." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4793047.

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Krishnakumar, K., P. Gonsalves, A. Satyadas, and G. Zacharias. "Hybrid fuzzy logic flight controller synthesis via pilot modeling." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-3227.

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Deng, Lujuan, and Huaishan Wang. "Fuzzy logic technology for modeling of greenhouse crop transpiration rate." In Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, edited by Jiancheng Fang and Zhongyu Wang. SPIE, 2006. http://dx.doi.org/10.1117/12.718289.

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Kebairi, A., M. Becherif, and M. El Bagdouri. "Modeling and PI-Fuzzy logic controller of the Pierburg mechatronic actuator." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5990978.

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Dariush, Behzad, and Kikuo Fujimura. "Fuzzy Logic Based Control Model of Human Postural Dynamics." In Digital Human Modeling For Design And Engineering Conference And Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2000. http://dx.doi.org/10.4271/2000-01-2178.

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Reports on the topic "Turbojet, Modeling, Control, Fuzzy Logic"

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Rajagopalan, A., G. Washington, G. Rizzoni, and Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/15006009.

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