Journal articles: 'Turbojet, Modeling, Control, Fuzzy Logic'

1

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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Zadeh, Lotfi A. "The role of fuzzy logic in modeling, identification and control." Modeling, Identification and Control: A Norwegian Research Bulletin 15, no. 3 (1994): 191–203. http://dx.doi.org/10.4173/mic.1994.3.9.

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Emami, M. Reza, Andrew A. Goldenberg, and I. Burhan Türksen. "Fuzzy-logic control of dynamic systems: from modeling to design." Engineering Applications of Artificial Intelligence 13, no. 1 (February 2000): 47–69. http://dx.doi.org/10.1016/s0952-1976(99)00031-7.

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Chiou, Yu-Chiun, and Yen-Fei Huang. "Genetic fuzzy logic traffic signal control with cell transmission modeling." Journal of the Chinese Institute of Engineers 37, no. 4 (July 25, 2013): 446–60. http://dx.doi.org/10.1080/02533839.2013.814995.

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Li, Han-Xiong, Xiaogang Duan, and Zhi Liu. "Three-dimensional fuzzy logic system for process modeling and control." Journal of Control Theory and Applications 8, no. 3 (July 25, 2010): 280–85. http://dx.doi.org/10.1007/s11768-010-0023-x.

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Du, P., J. Gong, E. SyrkinWurtele, and J. A. Dickerson. "Modeling Gene Expression Networks Using Fuzzy Logic." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, no. 6 (December 2005): 1351–59. http://dx.doi.org/10.1109/tsmcb.2005.855590.

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Atia, Doaa M., Faten H. Fahmy, Ninet M. Ahmed, and Hassen T. Dorrah. "Modeling and Control PV-Wind Hybrid System Based On Fuzzy Logic Control Technique." TELKOMNIKA (Telecommunication Computing Electronics and Control) 10, no. 3 (September 1, 2012): 431. http://dx.doi.org/10.12928/telkomnika.v10i3.821.

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Bastian, Andreas. "Modeling Fuel Injection Control Maps Using Fuzzy Logic and Neural Networks." Journal of Robotics and Mechatronics 6, no. 4 (August 20, 1994): 340–44. http://dx.doi.org/10.20965/jrm.1994.p0340.

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Determining the correct ignition point of the air-fuel mixture is critical in order to achieve maximum output torque and to reduce exhaust emissions. In some fuel injection control systems the amount of air cannot be detected, thus, look-up tables are utilized, which contain the amount of air for given engine speed and inlet manifold pressure. In this paper, we model the look-up table using fuzzy logic. A neural network approach is used to identify the inputs of the fuzzy model.

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Kiriakidis, K., A. Tzes, A. Grivas, and Pei-Yuan Peng. "Modeling, plant uncertainties, and fuzzy logic sliding control of gaseous systems." IEEE Transactions on Control Systems Technology 7, no. 1 (1999): 42–55. http://dx.doi.org/10.1109/87.736749.

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Sugeno, M., and T. Yasukawa. "A fuzzy-logic-based approach to qualitative modeling." IEEE Transactions on Fuzzy Systems 1, no. 1 (February 1993): 7. http://dx.doi.org/10.1109/tfuzz.1993.390281.

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Azizi, Aydin, Kambiz Ghaemi Osgouie, Sajad Rashidnejhad, and Mostafa Cheragh. "Modeling of Melatonin Behavior in Major Depression: A Fuzzy Logic Modeling." Applied Mechanics and Materials 367 (August 2013): 317–21. http://dx.doi.org/10.4028/www.scientific.net/amm.367.317.

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According to the world health organization, major depressive disorder (MDD) is considered as the fourth main cause of death and premature weakness in the whole world. Abnormality in the hormones and neurotransmitters level is the one of the main factors which may result in this disorder. In this article melatonin is chosen among these hormones, which is the most implicated to control sleep and depression. Because the measurement of melatonin is crucial important, the fuzzy logic approach as the mathematical method is utilized to making melatonin behavior model. In this paper, two effective factors on melatonin are modeled by fuzzy logic. This model is only a part of our project which is performed for modeling of the major depression.

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Lo, Ji-Chang, and Min-Long Lin. "Robust H∞ nonlinear modeling and control via uncertain fuzzy systems." Fuzzy Sets and Systems 143, no. 2 (April 2004): 189–209. http://dx.doi.org/10.1016/s0165-0114(03)00023-x.

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Reza Pishvaie, Mahmoud, and Mohammad Shahrokhi. "Control of pH processes using fuzzy modeling of titration curve." Fuzzy Sets and Systems 157, no. 22 (November 2006): 2983–3006. http://dx.doi.org/10.1016/j.fss.2006.05.010.

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Bazazzadeh, Mehrdad, and Ali Shahriari. "Enhancing the Performance of Jet Engine Fuel Controller Using Neural Networks." Applied Mechanics and Materials 390 (August 2013): 393–97. http://dx.doi.org/10.4028/www.scientific.net/amm.390.393.

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This paper proposes a fuzzy logic controller for a specific turbojet engine. The turbine engines require control systems to achieve the appropriate performance. The control systems typically featured loops to prevent engine flame out, over speeds, compressor surge, and check turbine inlet temperature limit, either by scheduling the fuel flow during accelerations and decelerations or by controlling the acceleration and deceleration rates of engine spool. This paper presents a successful approach in designing a Fuzzy Logic Controller for a specific Jet Engine. At first a suitable mathematical model for the jet engine is presented by the aid of SIMULINK simulation software. Then by applying different reasonable fuel flow functions via the engine model, some important engine continuous time operation parameters (such as: thrust, compressor surge margin, turbine inlet temperature and engine spool speed...) are obtained. These parameters provide a precious database which can be used by 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 design a fuzzy logic controller 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 fuzzy controller in comparison with the engine testing operation illustrate that the proposed controller achieves the desired performance.

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Farana, Radim, Bogdan Walek, Michal Janošek, and Jaroslav Žáček. "Use of Linguistic Fuzzy-Logic Control for Mechatronic System Modelling and Control." Applied Mechanics and Materials 816 (November 2015): 3–8. http://dx.doi.org/10.4028/www.scientific.net/amm.816.3.

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The article presents use of a linguistic fuzzy-logic control (LFLC) system for mechatronic system modelling and control. The presented applications were verified on real laboratory tasks in the Laboratory of Intelligent Systems at the University of Ostrava. The LFLC system was developed at the University of Ostrava, Institute for Research and Applications of Fuzzy Modeling. This technology enables users to describe the system behaviour and/or the control strategy as a set of fuzzy rules. Input and output variables scales are defined by contexts and their change allows using the same system description for systems with similar behaviour very easily.

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John, R. I., and P. R. Innocent. "Modeling Uncertainty in Clinical Diagnosis Using Fuzzy Logic." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, no. 6 (December 2005): 1340–50. http://dx.doi.org/10.1109/tsmcb.2005.855588.

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Lou, H. H., and Y. L. Huang. "Fuzzy-logic-based process modeling using limited experimental data." Engineering Applications of Artificial Intelligence 13, no. 2 (April 2000): 121–35. http://dx.doi.org/10.1016/s0952-1976(99)00057-3.

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Sharma, Anshul, C. K. Susheel, Rajeev Kumar, and V. S. Chauhan. "Fuzzy Logic Based Active Vibration Controller." Applied Mechanics and Materials 367 (August 2013): 357–62. http://dx.doi.org/10.4028/www.scientific.net/amm.367.357.

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This paper presents fuzzy logic approach for active vibration control of composite shell structure using collocated piezoelectric sensor/actuator. The vibratory response of piezolaminated composite shell is modeled using degenerated finite shell element. Modeling is based upon first order shear deformation theory and linear piezoelectric theory. The fuzzy IF-THEN rules are established on analysis of the motion traits of laminated composite shell. The fuzzy logic controller (FLC) is designed using the sensor voltage and its derivative as inputs and actuator voltage as output. The simulation results illustrate that this controller has more superiority than the conventional controller.

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Zhang, Chun Yang, Jin Nan Zhang, and Cai Qin Sun. "Fuzzy Logic Control of the Marine Permanent Magnet Synchronous Propulsion Motor with Direct Torque Control System." Advanced Materials Research 889-890 (February 2014): 920–24. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.920.

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As to the ripple problem of the direct torque control system of permanent magnet synchronous motor, this paper present a new method based on the fuzzy logic control. The new method replaces the traditional hysteresis torque controller by a fuzzy logic controller to realize the low ripple effect of the torque and the flux linkage. Also this paper modeling and simulate the new control system using the FIS toolbox provided by the Matlab/Simulink software, and the simulation results shows that the fuzzy control method is far more better than the traditional direct torque control system.

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Gao, Xiangdong. "Artificial neural network and fuzzy logic controller for GTAW modeling and control." Chinese Journal of Mechanical Engineering (English Edition) 15, no. 01 (2002): 53. http://dx.doi.org/10.3901/cjme.2002.01.053.

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30

Jian Tan, Hong Xie, and Yung-Cheng Lee. "Efficient establishment of a fuzzy logic model for process modeling and control." IEEE Transactions on Semiconductor Manufacturing 8, no. 1 (1995): 50–61. http://dx.doi.org/10.1109/66.350757.

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Lin, Y. J., Y. Lu, T. Lee, and B. Choi. "Modeling and Fuzzy Logic Control of an Active Reaction Compensating Platform System." Shock and Vibration 2, no. 6 (1995): 493–506. http://dx.doi.org/10.1155/1995/829607.

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This article presents the application of the fuzzy logic (FL) concept to the active control of a multiple degree of freedom reaction compensating platform system that is designed and used for isolating vibratory disturbances of space-based devices. The physical model used is a scaled down two-plate platform system. In this work, simulation is performed and presented. According to the desired performance specifications, a full range of investigation regarding the development of an FL stabilization controller for the system is conducted. Specifically, the study includes four stages: comprehensive dynamic modeling of the reaction compensating system; analysis of the dynamic responses of the platform system when it is subjected to various disturbances; design of an FL controller capable of filtering the vibratory disturbances transmitted to the bottom plate of the platform system; performance evaluation of the developed FL controller through computer simulations. To simplify the simulation work, the system model is linearized and the system component parameter variations are not considered. The performance of the FL controller is tested by exciting the system with an impulsive force applied at an arbitrarily chosen point on the top plate. It is shown that the proposed FL controller is robust in that the resultant active system is well stabilized when subjected to a random external disturbance. The comparative study of the performances of the FL controlled active reaction and passive reaction compensating systems also reveals that the FL controlled system achieves significant improvements in reducing vibratory accelerations over passive systems.

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Giorleo, G., F. Memola Capece Minutolo, and V. Sergi. "Fuzzy logic modeling and control of steel rod quenching after hot rolling." Journal of Materials Engineering and Performance 6, no. 5 (October 1997): 599–604. http://dx.doi.org/10.1007/s11665-997-0051-y.

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Lima, Nádson M. N., Lamia Zuñiga Liñan, Rubens Maciel Filho, Maria R. Wolf Maciel, Marcelo Embiruçu, and Filipe Grácio. "Modeling and predictive control using fuzzy logic: Application for a polymerization system." AIChE Journal 56, no. 4 (November 9, 2009): 965–78. http://dx.doi.org/10.1002/aic.12030.

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Ussama, Belarussi, V. F. Kalinin, and Amel Terki. "Effective Fuzzy Logical Control for Photoelectric System Optimization." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 27, no. 1 (2021): 062–72. http://dx.doi.org/10.17277/vestnik.2021.01.pp.062-072.

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The article shows the importance of tracking the point of maximum power and ways to achieve it. Methods of “perturbation and observation” and a fuzzy logic regulator (FLR) are analyzed. The modeling of the photovoltaic system operation in various conditions is carried out and the principle of its operation is considered.

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Anderson, D., R. H. Luke, J. M. Keller, M. Skubic, M. J. Rantz, and M. A. Aud. "Modeling Human Activity From Voxel Person Using Fuzzy Logic." IEEE Transactions on Fuzzy Systems 17, no. 1 (February 2009): 39–49. http://dx.doi.org/10.1109/tfuzz.2008.2004498.

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36

Zhou, Jinglei, and Qunli Zhang. "Adaptive Fuzzy Control of Uncertain Robotic Manipulator." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/4703492.

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This paper designs a kind of adaptive fuzzy controller for robotic manipulator considering external disturbances and modeling errors. First, n-link uncertain robotic manipulator dynamics based on the Lagrange equation is changed into a two-order multiple-input multiple-output (MIMO) system via feedback technique. Then, an adaptive fuzzy logic control scheme is studied by using sliding theory, which adopts the adaptive fuzzy logic systems to estimate the uncertainties and employs a filtered error to make up for the approximation errors, hence enhancing the robust performance of robotic manipulator system uncertainties. It is proved that the tracking errors converge into zero asymptotically by using Lyapunov stability theory. Last, we take a two-link rigid robotic manipulator as an example and give its simulations. Compared with the existing results in the literature, the proposed controller shows higher precision and stronger robustness.

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Sharma, Anshul, C. K. Susheel, Rajeev Kumar, and V. S. Chauhan. "Active Control of Thermally Induced Vibrations in Smart Structure Instrumented with Piezoelectric Materials." Applied Mechanics and Materials 612 (August 2014): 169–74. http://dx.doi.org/10.4028/www.scientific.net/amm.612.169.

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In this paper, a finite element model of piezolaminated composite shell structure is developed using nine-noded degenerated shell element. The stiffness, mass and thermo-electro-mechanical coupling effect is incorporated in finite element modeling using first order shear deformation theory and linear piezoelectric theory. The sensor voltage is calculated using the same formulation and fuzzy logic controller is used to calculate the actuator voltage. The fuzzy logic controller is designed as double input-single output (DISO) system using 49 If-Then rules. The performance of fuzzy logic controller is compared with convention constant-gain negative feedback controller. The simulation results illustrate the superiority of fuzzy logic controller over constant-gain negative feedback controller.

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Chuang, Chen-Tse, Shou-Zen Fan, and Jiann-Shing Shieh. "Rule extraction by fuzzy modeling algorithm for fuzzy logic control of cisatracurium as a neuromuscular block." Engineering Applications of Artificial Intelligence 22, no. 1 (February 2009): 129–40. http://dx.doi.org/10.1016/j.engappai.2008.05.011.

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39

Elshafei, Abdel Latif. "Robust Longitudinal Aircraft- Control Based on an Adaptive Fuzzy-Logic Algorithm." Sultan Qaboos University Journal for Science [SQUJS] 7, no. 1 (June 1, 2002): 187. http://dx.doi.org/10.24200/squjs.vol7iss1pp187-198.

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To study the aircraft response to a fast pull-up manoeuvre, a short period approximation of the longitudinal model is considered. The model is highly nonlinear and includes parametric uncertainties. To cope with a wide range of command signals, a robust adaptive fuzzy logic controller is proposed. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, robustifying and adaptive components are included in the control law to compensate for modeling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearity. The second system is an adaptive one that compensates for modeling errors. The derivation of the control law based on a dynamic game approach is given in detail. Stability of the closed-loop control system is also verified. Simulation results based on an F16-model illustrate a successful tracking performance of the proposed controller.

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KrishnaKumar, K., P. Gonsalves, A. Satyadas, and G. Zacharias. "Hybrid fuzzy logic flight controller synthesis via pilot modeling." Journal of Guidance, Control, and Dynamics 18, no. 5 (September 1995): 1098–105. http://dx.doi.org/10.2514/3.21510.

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Hasana, Sameh F., and Hassan M. Alwan. "Modeling and Control of Wheeled Mobile Robot With Four Mecanum Wheels." Engineering and Technology Journal 39, no. 5A (May 25, 2021): 779–89. http://dx.doi.org/10.30684/etj.v39i5a.1926.

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This work presents a driving control for the trajectory tracking of four mecanum wheeled mobile robot (FMWMR). The control consists of Backstepping-Type 1 Fuzzy Logic-Particle swarm optimization i.e.,(BSC-T1FLC-PSO). The kinematic and dynamic models have been derived. Backstepping controller (BSC) is used for finding controlled torques that generated from robot motors while Type-1 fuzzy logic control (T1FLC) as well as particle swarm optimization (PSO) used for finding the appropriate values of gain parameters of BSC. Square trajectory has been selected to test the performance of the control system of FMWMR. MATLAB/ Simulink is used to simulate the results. It has been concluded from the results that obtained from this control system there is a good matching between the simulated and the desired trajectories.

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Kochkin, Dmitry, and Aleksei Sukonschicov. "Fuzzy output system on the basis of the modified fuzzy Petri nets." E3S Web of Conferences 161 (2020): 01033. http://dx.doi.org/10.1051/e3sconf/202016101033.

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The development of information technologies requires improvement of simulation methods and mathematical apparatus. The mathematical apparatus of Petri nets is used for simulation of parallel asynchronous systems and has a broad scope. The modified fuzzy Petri nets expand the modeling capabilities of Petri nets by combining the properties of different extensions. Modified extension can serve for construction of models with a complex structure and logic of operation with the use of fuzzy logic apparatus for control on the basis of the system of production rules.

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Vachtsevanos, George, Wonoh Kim, Sami A. Al-Hasan, Freeman Rufus Jr., Miguel Simon, Daniel P. Schrage, and J. V. R. Prasad. "Mission Planning and Flight Control: Meeting the Challenge with Intelligent Techniques." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (October 20, 1997): 62–70. http://dx.doi.org/10.20965/jaciii.1997.p0062.

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This paper presents a hybrid hardware/software platform that supports flight control and mission planning algorithms for an autonomous helicopter. The emphasis is on the use of intelligent fuzzy logic based techniques and an object-modeling approach to account for unmodeled dynamics to address uncertainty issues and to provide a flexible platform for development purposes and an operator interface. Fuzzy logic routines are implemented in such critical vehicle modules as the route planner the fuzzy navigator the fault-tolerant tools and the flight controller.

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Zyada, Zakarya, Yasuhisa Hasegawa, Gancho Vachkov, and Toshio Fukuda. "Implementing Fuzzy Learning Algorithms in a 6 DOF Hydraulic Parallel Link Manipulator: Actuators' Fuzzy Modeling." Journal of Robotics and Mechatronics 14, no. 4 (August 20, 2002): 408–19. http://dx.doi.org/10.20965/jrm.2002.p0408.

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A fuzzy-logic-based model, suitable for force control, for each hydraulic actuator of a parallel link manipulator is presented. Constructing the fuzzy model rule base mainly consists of 2 stages: (1) learning rules from examples for the known acquired input/output data of the hydraulic actuators and (2) completing unknown fuzzy rules from heuristics and experience based on the logic of actuators' behavior. We first present the algorithm of fuzzy-rule base modeling and its application for one actuator. We then present fuzzy rule base results characterizing each hydraulic actuator, differing from one to another, of a 6 DOF parallel link manipulator. Simulation output results from fuzzy models show good agreement with experimental results.

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Xue, Chi, Hui Zhu, and Biao Yu. "Modeling and Simulation of Parameter Self-Tuning Fuzzy PID Controller for DC Motor Speed Control System." Applied Mechanics and Materials 195-196 (August 2012): 1003–7. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1003.

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The establishment of DC motor system model is an important part of its control system analysis, this paper introduces the traditional method of modeling and the application of parameter self-tuning fuzzy logic PID control in the simulation and experimental research of variable speed control system for DC electric motor. In MATLAB / SMULINK simulation environment, high robustness and precision are obtained. The simulation results show that fuzzy logic PID control strategy has better performances than traditional controllers.

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Chaibakhsh, A., M. Pourbeheshtian, M. J. Javadi Sigaroudi, and H. R. Najafi. "Modeling and Fuzzy Control of a Crude Oil Preheating Furnace." Applied Mechanics and Materials 229-231 (November 2012): 2370–74. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2370.

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This paper presents the development of mathematical model and designing a temperature control system for an industrial preheating furnace. In the first part of the paper, the simulation model was developed based on thermodynamics principles, energy-mass balance and semi-empirical relations. The parameters of developed models were defined with respect to available operational and geometrical data from real system. In the second part, an appropriate control system was designed for regulating the preheating furnace temperature. A fuzzy logic controller and a feedback/feedforward controller were employed for operating in coordination with each other to maintain the process outlet temperature around 360 oC. Simulation results show the capability of the designed control system to regulate the furnace outlet temperature at different operating conditions and in the presence of disturbances.

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Li, Na, Hai Peng Nan, Xiang Yang Yu, and Li Su. "Research on Modeling and Control Methods of Variable-Speed Variable-Pitch Wind Turbine Generators." Advanced Materials Research 383-390 (November 2011): 2636–43. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2636.

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In view of the high-order, nonlinear, strong coupling, multivariable and time varying character of wind energy conversion system, the fuzzy-logic control strategy is introduced in this paper. First, this thesis analyzes and establishes the mechanism model for each component element of the megawatt class variable-speed variable-pitch wind turbines as well as the wind speed model. Second, for the goal of maximum wind power extraction under rated wind and peak power output maintaining above rated speed wind, PID controllers as well as fuzzy-logic controllers are designed respectively by different wind velocity condition. The simulation results demonstrate that no matter for the following up performance or the anti-interference capability, the fuzzy controllers which are devised in the paper have a better control effect than PID controllers. The fuzzy controllers can reduce system’s overshoot and regulation time more effectively, offset the negative impacts of nonlinearity, and they have better robustness too.

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Kim, Jung-Chul, Won-Hyok Lee, and Jin-Kwon Kim. "Modeling and Control of Intersection Network using Real-Time Fuzzy Temporal Logic Framework." Journal of Institute of Control, Robotics and Systems 13, no. 4 (April 1, 2007): 352–57. http://dx.doi.org/10.5302/j.icros.2007.13.4.352.

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Deif, Tarek Naem, Ayman H. Kassem, and Gamal M. El Baioumi. "Modeling, Robustness, and Attitude Stabilization of Indoor Quad Rotor Using Fuzzy Logic Control." International Review of Aerospace Engineering (IREASE) 7, no. 6 (December 31, 2014): 192. http://dx.doi.org/10.15866/irease.v7i6.4306.

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Yoon-Ho Kim and Sang-Sun Kim. "An electrical modeling and fuzzy logic control of a fuel cell generation system." IEEE Transactions on Energy Conversion 14, no. 2 (June 1999): 239–44. http://dx.doi.org/10.1109/60.766989.

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

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