Journal articles: 'Control methodology][Fuzzy logic'

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Costa Drumond Sousa, Gilberto, Bimal K. Bose, and Marcelo Godoy Simões. "A simulation-implementation methodology of a fuzzy logic based control system." Eletrônica de Potência 2, no. 1 (June 1, 1997): 61–68. http://dx.doi.org/10.18618/rep.1997.1.061068.

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Korchunov, Alexey, Mikhail Chukin, and Aleksandr Lysenin. "Methodology of Developing Mathematical Models with Fuzzy Logic Elements for Quality Indices Control." Applied Mechanics and Materials 436 (October 2013): 374–81. http://dx.doi.org/10.4028/www.scientific.net/amm.436.374.

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Advantages of applying fuzzy logic theory to metal products quality indices control in development of new models and in improvement of acting process operations are shown. It is proved that it is appropriate to determine fuzzy relation as preference relation in process of handling products quality indices in process operations. Elaboration of algorithm of handling mathematical models with fuzzy logic elements to control quality indices is undertaken. Methodology of mathematical models development with fuzzy logic elements for metal products quality indices control is created. Process of metal products quality indices control on the basis of models with fuzzy logic elements is illustrated.

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Touati, Y., and Y. Amirat. "Fuzzy logic controller design methodology for Cartesian robot control." International Journal of Computer Applications in Technology 27, no. 2/3 (2006): 85. http://dx.doi.org/10.1504/ijcat.2006.011135.

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Sharma, Kaushik Das. "A systematic design methodology of PD fuzzy logic controller using cellular fuzzy logic concept." International Journal of Automation and Control 6, no. 3/4 (2012): 231. http://dx.doi.org/10.1504/ijaac.2012.051882.

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Andújar, José M., and Antonio J. Barragán. "A methodology to design stable nonlinear fuzzy control systems." Fuzzy Sets and Systems 154, no. 2 (September 2005): 157–81. http://dx.doi.org/10.1016/j.fss.2005.03.006.

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Preye, Uguta Henry, and Onyejegbu Laeticia Nneka. "An Intelligent Fuzzy Logic System for Network Congestion Control." Circulation in Computer Science 2, no. 11 (December 20, 2017): 23–30. http://dx.doi.org/10.22632/ccs-2017-252-69.

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Network congestion is a major problem in all network environments as such it calls for ways to manage this problem. In this paper, we propose a Fuzzy Regulator Effective Random Early Detection (FRERED) system, which is an intelligent fuzzy logic based controller technique for early stage congestion detection, at the router buffer in the networks. The proposed technique extends the Fuzzy-Based system in the Fuzzy Hybrid ERED algorithm by considering the delay variable in its inference system to ease the problem of parameter initialization and parameter dependency. Unlike the Fuzzy-Based controller in the existing Fuzzy Hybrid ERED system which uses two parameter settings in its inference system that is, the queue size and average queue length in computing the dropping probability of packets. The proposed technique uses the queue size, average queue length and the delay approximation as input variables in computing the packet drop probability. The applied fuzzy logic system yields an output that denotes a packet dropping probability, which in turn controls and prevents congestion in early stage. This was achieved after simulating the proposed technique and the existing Fuzzy-Based controller using Matlab. The results obtained shows that this approach results in less packet drops for about the same link utilization as the existing Fuzzy-Based controller. Therefore, this technique, generally, controls network congestion and improves network performance. The methodology used to achieve this is the object oriented methodology and JAVA programming language was used to develop the system.

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Zeinali, Meysar, and Leila Notash. "FUZZY LOGIC-BASED INVERSE DYNAMIC MODELLING OF ROBOT MANIPULATORS." Transactions of the Canadian Society for Mechanical Engineering 34, no. 1 (March 2010): 137–50. http://dx.doi.org/10.1139/tcsme-2010-0009.

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This paper presents the design and implementation of a systematic fuzzy modelling methodology for the inverse dynamic modelling of robot manipulators. The fuzzy logic modelling methodology is motivated in part by the difficulties encountered in the modelling of complex nonlinear uncertain systems, and by the objective of developing an efficient dynamic model for the real-time model-based control. The methodology is applied to build the fuzzy logic-based inverse dynamic model of a prototyped wire-actuated parallel manipulator with uncertain dynamics. The developed inverse dynamics has been used in a fuzzy model-based adaptive robust controller for the tracking control of the parallel manipulator.

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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (June 8, 2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

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Kim, Euntai, Heejin Lee, and Dongyon Kim. "Fuzzy Control of a Direct Current Motor System and Stability Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 515–18. http://dx.doi.org/10.20965/jaciii.1999.p0515.

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One of the most common ways of driving electromechanical systems is through the use of a DC motor. In this paper, fuzzy control methodology for a DC motor system using a singleton fuzzy logic controller (FLC) is proposed. As opposed to conventional works, fuzzy control methodology proposed here is guaranteed to be asymptotically stable on the whole. Finally, the validity of the suggested methodology is highlighted via an illustrative example.

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Mendes, Jérôme, Ricardo Maia, Rui Araújo, and Francisco A. A. Souza. "Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules." Applied Sciences 10, no. 17 (August 23, 2020): 5836. http://dx.doi.org/10.3390/app10175836.

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The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

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Abdalla, M. O., and T. A. Al–Jarrah. "Autogeneration of Fuzzy Logic Rule-Base Controllers." Applied Mechanics and Materials 110-116 (October 2011): 5123–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5123.

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A novel Fuzzy Logic controller design methodology is presented. The method utilizes a Particle Swarm Optimization (PSO) binary search algorithm to generate the rules for the Fuzzy Logic controller rule-base stage without human experience intervention. The proposed technique is compared with the well established Lyapunov based Fuzzy Logic controller design in generating the rules. Finally, the controller’s effectiveness and performance are tested, verified and validated using an elevator control application. The novel controller’s results are to be compared with traditional Proportional Integral Derivative (PID) controller and classical Fuzzy Logic (FL) controllers.

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Mekrini, Zineb, and Seddik Bri. "Fuzzy Logic Application for Intelligent Control of An Asynchronous Machine." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (July 1, 2017): 61. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp61-70.

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<p>The aim of this article is propose a method to improve the direct torque control and design a Fuzzy Logic based Controller which can take necessary control action to provide the desired torque and flux of an asynchronous machine. It’s widely used in the industrial application areas due to several features such as fast torque response and less dependence on the rotor parameters. The major problem that is usually associated with DTC control is the high torque ripple as it is not directly controlled. The high torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. The fuzzy logic controller is applied to reduce electromagnetic torque ripple. In this proposed technique, the two hysteresis controllers are replaced by fuzzy logic controllers and a methodology for implementation of a rule based fuzzy logic controller are presented. The simulation by Matlab/Simulink was built which includes induction motor d-q model, inverter model, fuzzy logic switching table and the stator flux and torque estimator. The validity of the proposed method is confirmed by the simulative results of the whole drive system and results are compared with conventional DTC method. </p>

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Boukabou, Abdelkrim, and Noura Mansouri. "T-S Fuzzy Control of Uncertain Chaotic Vibration." Shock and Vibration 19, no. 3 (2012): 379–89. http://dx.doi.org/10.1155/2012/368207.

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We present in this paper a novel and unified control approach that combines intelligent fuzzy logic methodology with predictive method for controlling chaotic vibration of a class of uncertain chaotic systems. We first introduce prediction into each subsystem of Takagi Sugeno (T-S) fuzzy IF-THEN rules and then present a unified T-S predictive fuzzy model for chaos control. The proposed controller can successfully stabilize the chaos and track the desired targets. The simulation results illustrate its effectiveness.

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Ursu, Ioan, Felicia Ursu, and Lucian Iorga. "Neuro‐fuzzy synthesis of flight control electrohydraulic servo." Aircraft Engineering and Aerospace Technology 73, no. 5 (October 1, 2001): 465–72. http://dx.doi.org/10.1108/00022660110403014.

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Presents a switching type neuro‐fuzzy control synthesis. The control algorithm supposes as a component part a neurocontrol designed to optimize a performance index. Whenever the neurocontrol saturates or a certain performance parameter of the system decreases, the scheme of control switches to a feasible and reliable fuzzy logic control. Describes the procedure of return to the optimizing neurocontrol which is essential. This methodology of control synthesis ensures antisaturating, antichattering and robustness properties of the controlling system, as illustrated by numerical simulation in the case of a primary flight controls electrohydraulic servo actuator

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CHORTARAS, ALEXANDROS, GIORGOS STAMOU, and ANDREAS STAFYLOPATIS. "DEFINITION AND ADAPTATION OF WEIGHTED FUZZY LOGIC PROGRAMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, no. 01 (February 2009): 85–135. http://dx.doi.org/10.1142/s0218488509005759.

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Fuzzy logic programming has been lately used as a general framework for representing and handling imprecise knowledge. In this paper, we define the syntax and the semantics of definite weighted fuzzy logic programs, which extend definite fuzzy logic programs by allowing the inclusion of different significance weights in the individual atoms that make up the antecedent of a fuzzy logic rule. The weights add expressiveness to a fuzzy logic program and allow the determination of the level up to which an atom in the antecedent of a rule may affect the truth value of its consequent. In describing the semantics of definite weighted fuzzy logic programs we introduce the notion of the generalized weighted fuzzy conjunction operator, which can be regarded as a weighted t-norm based aggregation. We determine the properties of generalized weighted fuzzy conjunction operators and provide several examples. A methodology for constructing generalized weighted fuzzy conjunction operators using generator functions of existing t-norms is also introduced. Finally, a method for setting up a parametric weighted fuzzy logic program and automatically adapting the weights of its rules using a numerical dataset is developed.

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Aengchuan, Prasert, and Busaba Phruksaphanrat. "Inventory System Design by Fuzzy Logic Control: A Case Study." Advanced Materials Research 811 (September 2013): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amr.811.619.

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Existing inventory lot-sizing models assume certain demand and sufficient supply, which are not practical for industry. Dynamic inventory models can serve uncertain demand, but supply is assumed to be available. However, in the real world situation, supply is not always offered. So, the method that can deal with both uncertain demand and supply should be developed. Fuzzy logic control is now being the effective methodology in many applications under uncertainty. Therefore, a fuzzy logic approach for solving the problem of inventory control under uncertainty was proposed for a case study factory. In the proposed Fuzzy Inventory System (FIS), both demand and availability of supply are described by linguistic terms. Then, the developed fuzzy rules are used to extract the fuzzy order quantity and the fuzzy reorder point continuously. The order quantity and reorder point are both adjusted according to the FIS system. In this research, the suitable ranges for the inputs of the FIS model are justified for the case study factory. Moreover, the effect of trend demands for both increase and decrease are also analyzed with the proposed range. Inventory costs of the proposed fuzzy inventory system are compared with the existing model based on historical data of the case study factory. It found that the proposed range can obtain lower cost than the previous research FIS lot-sizing model, which is better than conventional approaches.

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Karthikeyan, R., Sreekanth Pasam, S. Sudheer, and Vallabhaneni Teja. "Fuzzy Logic Based Set-Point Weighting for Fractional Order PID Control." Applied Mechanics and Materials 367 (August 2013): 369–76. http://dx.doi.org/10.4028/www.scientific.net/amm.367.369.

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Differentiation and integration of non-integer order have drawn increasing attention in research community. Fractional order dynamic systems have been recognized as effective tool for characterizing the real world phenomena. This may be implemented by using different control structures in which a fuzzy mechanism is adopted to tune the parameters by using Ziegler-Nichols method. Fractional-order PID control is the development of general integer-order PID controller. This paper proposes the basic framework of fractional order dynamic system with fuzzy weighted set-point. Comparisons are made with PID and FOPID controllers for first and second order systems. The response shows the superiority of the fuzzy set-point weighting methodology over the other methods.

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Fatima, Boutlilis, Chouitek Mama, and Bekkouche Benaissa. "Design methodology of smart photovoltaic plant." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 4718. http://dx.doi.org/10.11591/ijece.v11i6.pp4718-4730.

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In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.

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Fayaz, Muhammad, Israr Ullah, and DoHyeun Kim. "An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment." Electronics 8, no. 2 (January 28, 2019): 132. http://dx.doi.org/10.3390/electronics8020132.

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The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

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Vachtsevanos, G. "Idle Speed Control of an Automotive Engine Using a Systematic Fuzzy Logic Methodology." IFAC Proceedings Volumes 26, no. 2 (July 1993): 29–34. http://dx.doi.org/10.1016/s1474-6670(17)48676-9.

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T., Dr Vijayakumar, Mr Vinothkanna R., and Dr Duraipandian M. "Fuzzy Logic Based Aeration Control System for Contaminated Water." March 2020 2, no. 1 (March 10, 2020): 10–17. http://dx.doi.org/10.36548/jei.2020.1.002.

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The waste water which are the outcomes of processing liquids cannot be used further without proper treatment so the water has to be treated and handled in order to elude the contamination that deteriorates the quality of the atmosphere. Although the biological methods can be utilized for treating the wastes in the water by decomposing the bacteria, it is biased by various causes such as the impurity level, the oxygen available, the dirt type etc. But the standard methodology like aeration utilizes the biological and the chemical oxygen demand reduction termed BO and CO respectively to treat the waste water, the conventional aeration process is performed manually causing enormous usage of electrical energy. So the paper elaborates the scheme of a fuzzy logic based aerator control system (FLACS) for the waste water. The essential equipment of the proposed system are the sensors providing the particulars of the chemical and the biological oxygen demand as input, the microcontroller (Arduino UNO) and other electrical and electronic equipment’s that controls the working of the aerator. The analysis performed on the proffered model indicates the performance improvement of the FLACS in terms of the electrical energy utilization and duration of working hours.

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Li, Yi Min, and Yang Cai. "A New Adaptive Fuzzy Sliding Mode Control for Uncertain Nonlinear Systems." Advanced Materials Research 327 (September 2011): 12–16. http://dx.doi.org/10.4028/www.scientific.net/amr.327.12.

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A novel Adaptive Fuzzy Sliding Mode Control (AFSMC) methodology is proposed in this paper based on the integration of Sliding Mode Control (SMC) and Adaptive Fuzzy Control (AFC).To get rid of the chattering and the bound of uncertainty, an adaptive fuzzy logic system design method introduced for the switching gain is proposed. The main advantage of our proposed methodology is that the nonlinear systems are unknown and no knowledge of the bounds of parameters, uncertainties and external disturbance are not required in advance.the design for the switching gain which will relax the requirement for the bound of uncertainty can ensure stability. The simulation results illustrate the effectiveness of the method.

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Verij Kazemi, Mohammad, Morteza Moradi, and Reza Verij Kazemi. "Fuzzy logic control to improve the performance of the direct power control based DFIG." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 1/2 (December 20, 2013): 254–72. http://dx.doi.org/10.1108/compel-08-2012-0131.

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Purpose – A direct power control (DPC) of the doubly-fed induction generator (DFIG) is presented. A new method, which is based on the rotation of the space sector, clockwise or vice versa, is proposed to improve the performance of the switching table. Then, it is combined with a fuzzy system to have advantages of both rotation sector and fuzzy controller. The paper aims to discuss these issues. Design/methodology/approach – In this paper, a new DPC of the DFIG is presented. To improve the performance of the switching table, a new method is proposed. The method is based on the rotation of the space sector, clockwise or vice versa. The excellence of the proposed method is proven. Then, it is shown that the performance of the system can be enhanced by using a fuzzy logic controller. The rotation method is combined with a fuzzy system. Findings – Simulation shows that although sector rotation and fuzzy controller can improve the performance of the DFIG, a combination of both demonstrates a smoother response in order that reactive and active power ripples and THD of the injected current decrease in different speeds. Also, it is demonstrated that the proposed method is robust against parameters variations. However, a hardware experiment should be performed to be practically verified. Originality/value – A sector rotation is proposed and its effect on the performance of the DFIG is considered. A simple method to write rules table is presented and the performance of sector rotation and fuzzy controller on the DFIG is analysed.

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Wang, Ming-Shyan, Seng-Chi Chen, Wei-Chin Fang, and Po-Hsiang Chuang. "Torque ripple reduction of switched reluctance motor using fuzzy control." Engineering Computations 33, no. 6 (August 1, 2016): 1668–79. http://dx.doi.org/10.1108/ec-08-2015-0220.

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Purpose – Extensive efforts have been conducted on the improvement of torque ripple in switched reluctance motor (SRM) drive. The purpose of this paper is to estimate initial on time of pulse-width modulation (PWM) and turn-off angle using the motor speed and rotor angle by fuzzy logic. Design/methodology/approach – A fuzzy logic control together with the PWM technique and turn-off angle are used to improve torque ripple and dynamic response. Findings – After determining initial on time of PWM, the rise slope of phase current is increased. Research limitations/implications – Future work will consider to increase the complex of the fuzzy control to adaptively tune parameters and achieve excellent results. Practical implications – The experimental results of the proposed method are presented to show the effectiveness. Originality/value – This paper achieves SRM control by one special PWM technique which is seldom studied.

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Dongcheol Kim and Sehun Rhee. "Design of an optimal fuzzy logic controller using response surface methodology." IEEE Transactions on Fuzzy Systems 9, no. 3 (June 2001): 404–12. http://dx.doi.org/10.1109/91.928737.

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MENCAR, CORRADO, GIOVANNA CASTELLANO, and ANNA M. FANELLI. "ON THE ROLE OF INTERPRETABILITY IN FUZZY DATA MINING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 05 (October 2007): 521–37. http://dx.doi.org/10.1142/s0218488507004856.

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Data Mining, a central step in the broader overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential — yet rarely tackled — feature that makes resulting patterns accessible by end users. In this paper we argue that the adoption of Fuzzy Logic for Data Mining can improve understandability of derived patterns. Indeed, Fuzzy Logic is able to represent concepts in a “human-centric” way. Hence, Data Mining methods based on Fuzzy Logic may potentially meet the so-called “Comprehensibility Postulate”, which characterizes the blurry notion of understandability. However, the mere adoption of Fuzzy Logic for Data Mining is not enough to achieve understandability. This paper describes and comments a number of issues that need to be addressed to provide for understandable patterns. A careful consideration of all such issues may end up in a systematic methodology to discover comprehensible knowledge from data.

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Mandal, Manoj Kumar, Arun Prasad Burnwal, Neelam Dubey, and Om Prakash Dubey. "USE OF FUZZY MATHEMATICAL QUADRATIC PROGRAMMING APPROACH IN JOB EVALUATION." International Journal of Students' Research in Technology & Management 9, no. 2 (May 15, 2021): 25–29. http://dx.doi.org/10.18510/ijsrtm.2021.925.

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Purpose of study: The current paper is the based on mathematical model of the job evolution system. Methodology: The proposed method is the fusion of quadratic programming and fuzzy logic where quadratic programming is used to optimize objective function with related constraints in the form of non-linear formulation. Fuzzy logic is used to control uncertainty related information by estimating imprecise parameters Main Finding: The optimal solution of the job evaluation based on fuzzy environment where goal is imprecise. Application of this study: It is used in the areas where information is not exact. The originality of this study: The novelty of the method is the fusion of quadratic programming and fuzzy logic.

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Sobolewski, Michal, Norbert Grzesik, Zbigniew Koruba, and Michal Nowicki. "Fuzzy logic estimator implemented in observation-tracking device control." Aircraft Engineering and Aerospace Technology 88, no. 6 (October 3, 2016): 697–706. http://dx.doi.org/10.1108/aeat-09-2015-0206.

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Purpose Nowadays, various methods of observation from unmanned aerial vehicles (UAV) are being widely developed. There are many ways of increasing the amount of information retrieved from captured material. Unfortunately, hardware solutions consume a lot of energy, which is unacceptable in UAV applications, as it can have direct impact on the observing time on UAV. Those kinds of problems have been identified during the development phase of stabilizing platform in Polish Research Space Centre in Warsaw. As a result of that fact, energy saving control methods have been implemented, which estimates quality of stabilization process for the observation-tracking device (OTD). Design/methodology/approach Mathematical model has been designed and validated with real-life experiments for the purpose of optimization of stabilization and control process. Two types of controlling algorithms have been implemented: linear quadratic regulator and proportional derivative method for driving the mechanism. Based on numerical simulations of the mechanical model being controlled by the mentioned driver, it was possible to define membership functions. After the process of defuzzification, the controller predicts quality of stabilization under defined environmental working conditions. Findings An autonomous energy saving system has been created that can be implemented in many applications, where environmental conditions may change significantly. Practical implications To test the proposed fuzzy controller, OTD has been chosen as an example object of application. It is a mechanical platform which houses the optical observation system. It is designed to provide the best working conditions during flight. Originality/value That kind of decision-making unit has never been implemented before during observations which were carried out during flying of an object. That innovative controller should bring significant energy consumption savings.

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Makhloufi, Khadidja, Ismail Khalil Bousserhane, and Si Ahmed Zegnoun. "Adaptive fuzzy sliding mode controller design for PMLSM position control." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (June 1, 2021): 674. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp674-684.

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We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.

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Eide, Arne. "Substituting model-based indicators in Harvest Control Rules by observations using fuzzy logic methodology." ICES Journal of Marine Science 75, no. 3 (December 26, 2017): 977–87. http://dx.doi.org/10.1093/icesjms/fsx227.

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Abstract Harvest Control Rules are predefined heuristic decision rules to provide quota advices for managed fisheries. Frequently statistical methods and biological assumptions expressed in mathematical models, are used to provide the Harvest Control Rules with initial information (indicators values). The aim of this article is to investigate a possible way forward of replacing these inputs by quantities of measurable observations, e.g. catch-at-age statistics. The article presents a method by which recruitment indexes and stock biomass indicators are obtained by non-parametric use of annual catch-at-age records, without filtering the raw data (observations) through mathematical models. Two related methods, applied on three empirical cases, are provided: First, showing that recruitment strengths of the Northeast Arctic cod, haddock, and saithe stocks, obtained by fuzzy logic methodology, are satisfactory captures by the use of catch-at-age data. Second, stock size indicators are estimated for the three species by the same catch-at-age data. The second task turns out to be more challenging than the first, but also in the case of stock size evaluation, the suggested procedure provides reasonable results when compared to standard stock assessment methods.

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Shakya, Dr Subarna. "Vehicle Drive Control Using Fuzzy Based PI Speed Controller." Journal of Electrical Engineering and Automation 2, no. 2 (May 10, 2020): 68–75. http://dx.doi.org/10.36548/jeea.2020.2.002.

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This paper proposes a fuzzy PI speed controller which is used in electric vehicle’s drive control system insensitive to parameter change and disturbance, using fuzzy control theory. A permanent magnet synchronous motor (PMSM) is modelled mathematically in d-q reference frame. In this paper, a sliding model and fuzzy control theory are used to simulate the PMSM models using fuzzy control theory. Simulink software is used to analyze and simulate the simulation models which show that the proposed PI control based on fuzzy logic will have better anti-interference, better dynamic performance, and faster dynamic response speed when compared with the sliding motor control. Hence the proposed methodology is considered to be the ideal control method with a predefined vector control reference value for the electric vehicle’s motor.

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Sabo, Chelsea, and Kelly Cohen. "Fuzzy Logic Unmanned Air Vehicle Motion Planning." Advances in Fuzzy Systems 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/989051.

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There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV) being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent's sensing range) and target location and outputs a change in heading angle and speed. The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC) feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF) solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.

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Khezri, Rahmat, and Hassan Bevrani. "Stability Enhancement in Multi-Machine Power Systems by Fuzzy-based Coordinated AVR-PSS." International Journal of Energy Optimization and Engineering 4, no. 2 (April 2015): 36–50. http://dx.doi.org/10.4018/ijeoe.2015040103.

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This paper presents performance of intelligent fuzzy-based coordinated control for Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS), to prevent losing synchronism after major sudden faults and to achieve appropriate post-fault voltage level in multi-machine power systems. The AVR and PSS gains can adaptively change to guarantee the power system stability after faults. For change in AVR and PSS gains, at least one significant generator in each area of a multi-area power system is equipped with fuzzy logic unit. The fuzzy logic unit accepts normalized deviations of terminal voltage and phase difference of synchronous generators as inputs and generates the desirable gains for AVR and PSS. The construction of appropriate fuzzy membership functions and rules for best tuning of gains is described. The proposed fuzzy control methodology is applied to 11-bus 4-generator power system test case. Simulation results illustrate the effectiveness and robustness of the proposed fuzzy-based coordinated control strategy.

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Balanagu, P., and M. Umavani. "A Fuzzy-Logic Based Control Methodology in Microgrids in the Presence of Renewable Energy Units." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 280. http://dx.doi.org/10.14419/ijet.v7i2.20.14778.

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The mix of conveyed ages, for example, photovoltaic and wind and additionally substantial load varieties prompts the significant issue of recurrence soundness issue. This paper shows a multi-arrange recurrence control for microgrids. Vitality stockpiling frameworks, for example, BESSs are chosen as an adaptable and quick reaction gadget for this application. In the main stage, a PI control strategy in view of PSO for the BESS is connected so as to limit the recurrence deviations. Also, in possibility modes, in which the BESS with the enhanced PI control application can't balance out the framework because of the uneven circumstance of free market activity, quick response of the focal control framework administrator is essential so as to shield the system from crumple. Thus, in the second phase of the control, a Fuzzy-rationale recurrence controller as a brilliant controller is outlined. This controller proposes arrangements through power level change, for example, stack shedding in a brief time frame to save the system from instability. The proposed technique is approved by an arrangement of reproductions on a delegate microgrid. The viability of the proposed multi-organize control is delineated through the correlation with the one-arrange controller without the Fuzzy-rationale part.

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Yu, Lie, Jia Chen, Yukang Tian, Yunzhou Sun, and Lei Ding. "Fuzzy logic algorithm of hovering control for the quadrotor unmanned aerial system." International Journal of Intelligent Computing and Cybernetics 10, no. 4 (November 13, 2017): 451–63. http://dx.doi.org/10.1108/ijicc-02-2017-0009.

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Purpose The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems (UASs). In addition, the aim of using two PID controller is to achieve the position control and velocity control simultaneously. Design/methodology/approach The dynamic of the UASs is mathematically modeled. One PID controller is used for position tracking control, while the other is selected for the vertical component of velocity tracking control. Meanwhile, fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position. Findings Based on this fuzzy logic algorithm, the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero. The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground. Social implications The acquired results are based on simulation not experiment. Originality/value This is the first study to use two independent PID controllers to realize stable hovering control for UAS. It is also the first to use the velocity of the UAS to calculate the desired position.

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Zhang, Xian-Xia, Ye Jiang, Shiwei Ma, and Bing Wang. "Reference Function Based Spatiotemporal Fuzzy Logic Control Design Using Support Vector Regression Learning." Journal of Applied Mathematics 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/410279.

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This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR) learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF), which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.

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Hassan, Tehzeeb-ul, Rabeh Abbassi, Houssem Jerbi, Kashif Mehmood, Muhammad Faizan Tahir, Khalid Mehmood Cheema, Rajvikram Madurai Elavarasan, Farman Ali, and Irfan Ahmad Khan. "A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller." Energies 13, no. 15 (August 3, 2020): 4007. http://dx.doi.org/10.3390/en13154007.

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Photovoltaic (PV) is a highly promising energy source because of its environment friendly property. However, there is an uncertainty present in the modeling of PV modules owing to varying irradiance and temperature. To solve such uncertainty, the fuzzy logic control-based intelligent maximum power point tracking (MPPT) method is observed to be more suitable as compared with conventional algorithms in PV systems. In this paper, an isolated PV system using a push pull converter with the fuzzy logic-based MPPT algorithm is presented. The proposed methodology optimizes the output power of PV modules and achieves isolation with high DC gain. The DC gain is inverted into a single phase AC through a closed loop fuzzy logic inverter with a low pass filter to reduce the total harmonic distortion (THD). Dynamic simulations are developed in Matlab/Simulink by MathWorks under linear loads. The results show that the fuzzy logic algorithms of the proposed system efficiently track the MPPT and present reduced THD.

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Pratama, Ari Rizki, Delano Ariesagita Hutagalung, Wali Siregar, and Hendra Sihombing. "Monitoring patient health based on medical records using fuzzy logic method." SinkrOn 3, no. 2 (March 5, 2019): 20. http://dx.doi.org/10.33395/sinkron.v3i2.10014.

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The Fuzzy Logic concept was first introduced by Prof. Lotfi Zadeh from the University of California at Berkeley in 1965, and presented not as a control methodology, but as a way of processing data by allowing the use of partial set membership compared to the crisp set membership or non-membership. Along with the development of computer technology, the concept of fuzzy logic is increasingly needed by people, because this concept is able to provide information needed in the decision making process. This study aims to analyze and design intelligent systems to monitor the health development of inpatients. The method used is the Fuzzy Logic method. This method will predict the level (degree) of health of each patient based on the amount of drug use and the durationof diagnosis process. The tools used to analyze and design the system are Unified Modeling Language. The results of this research are monitoring the health development of patients using the Fuzzy Logic method.

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Toan Trinh, Dinh. "Fuzzy-based quantification of congestion for traffic control." Transport and Communications Science Journal 72, no. 1 (January 25, 2021): 1–8. http://dx.doi.org/10.47869/tcsj.72.1.1.

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This paper presents a methodology for appraisal of congestion level for traffic control on expressways using fuzzy logic. The congestion level indicates the severity of congestion and is estimated using speed and density, being the basic traffic parameters that describe state of a traffic stream. Formulation of the fuzzy rule base is made based on knowledge on traffic flow theory and engineering judgments. Field data on a segment of the Pan-Island Expressway of Singapore were used to estimate the congestion levels for three scenarios: single input variable (speed or density) and combined input variables (speed and density), represented by congestion level on a [0 1] scale. The results showed that there were big gaps between the congestion levels evaluated based specifically on speed and density alone (single state variable), and the congestion levels estimated from both variables lie in between. Given the uncertainty in traffic data collection and dynamic nature of traffic flow, this indicates that it may be inadequate to evaluate traffic congestion level using a single variable, and the use of both speed and density represent the state of a traffic stream more properly. The study results also show that the fuzzy logic approach provides flexible combination of state variables to obtain the congestion level and to describe gradual transition of traffic state, which is particularly important under the heavy congested conditions.

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Turc, Cristian Gheorghe, and George Belgiu. "Fuzzy Logic Applications in Flanges Manufacturing." Advanced Materials Research 837 (November 2013): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.837.223.

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The industrial engineering experts consider the product early design stage as one of the most important stage of the design process, because it influences all stages of the product life cycle. One of the capital questions for manufacturing or design engineering people is how simple or elaborated work piece to use for a given part, in a specific set of production conditions. The optimization problem consists of the choosing of the right work piece for the current production conditions. In many cases this problem is solved empirically, based on the experience of the manufacturer. This approach leads to results that are situated more or less close to the optimum for the technical and economical point of view. Fuzzy logic is a method that is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. For the parts manufacturing, the main idea of the algorithm is to formalize the knowledge regarding production volume, geometry, loading conditions and other factors, using fuzzy sets and then to take the work piece choosing decision through the inference rules that are specified to fuzzy logic methodology. The described fuzzy logic algorithm allows a rapid, argued choosing of the work piece type in the production. The paper describes the method implementation for the manufacturing of flanges, including the description of the software results.

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Turc, Cristian Gheorghe, Felicia Banciu, and George Belgiu. "Fuzzy Logic Applications in Gears Manufacturing." Advanced Materials Research 1036 (October 2014): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.1028.

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The experts of industrial design field consider the product early design stage as one of the most important stage of the design process, because it influences all stages of the product life cycle. One of the capital questions for manufacturing or design engineering people is how simple or elaborated work piece to use for a given part, in a specific set of production conditions. The optimization problem consists of the choosing of the right work piece for the current production conditions. In many cases this problem is solved empirically, based on the experience of the manufacturer. This approach leads to results that are situated more or less close to the optimum for the technical and economical point of view. Fuzzy logic is a method that is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. For the parts manufacturing, the main idea of the algorithm is to formalize the knowledge regarding production volume, loading conditions and other factors, using fuzzy sets and then to take the work piece choosing decision through the inference rules that are specified to fuzzy logic methodology. The described fuzzy logic algorithm allows a rapid, argued choosing of the work piece type in the production. The paper describes the method implementation for the manufacturing of gears, including the description of the software results and their interpretation.

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Rovatti, R., and C. Fantuzzi. "IJAR Special Issue dedicated to the International Summer School: Fuzzy logic control advances in methodology." International Journal of Approximate Reasoning 22, no. 1-2 (September 1999): 1–2. http://dx.doi.org/10.1016/s0888-613x(99)00014-6.

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L. Iliev, Oliver, Pavle Sazdov, and Ahmad Zakeri. "A fuzzy logic-based controller for integrated control of protected cultivation." Management of Environmental Quality: An International Journal 25, no. 1 (January 7, 2014): 75–85. http://dx.doi.org/10.1108/meq-06-2013-0065.

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Purpose – The purpose of this paper is to present a fuzzy logic-based control system for controlling the protected cultivation and describes its advantages over the traditional greenhouse automation control systems. Design/methodology/approach – In terms of systems theory, the greenhouse represents a complex non-linear system with emphasized subsystem interactions. Applying a non-linear control encompasses a number of difficulties due to incomplete knowledge of system dynamic. System decoupling is used in order to obtain simplified control structures for independent control loops. This gives limited results due to strong interaction between system variables and such control system does not allow optimization of system behavior primarily in terms of energy efficiency and/or water consumption. Findings – The paper presents a design of fuzzy logic-based controller, which optimizes the greenhouse energy and water consumption. The design includes the main linguistic variables for sensor and actuator subsystems. Membership functions of Fuzzy Inference System (FIS) are generated and simulation and analysis of the behavior of the designed control system is performed. Research limitations/implications – Obtained result shows that the designed control system beside its relative simplicity is flexible and adaptive, taking into account the differences in crop varieties and growth stages. Practical implications – Preliminary simulation of energy savings compared with the costs on actual field shows also good results. Still, number of different control strategies has to be applied in order to increase system flexibility regarding the different varieties and different stages of their growth. Originality/value – Obtaining an integrated controller based on fuzzy logic will highly improve possibilities for mass production of cheap technology. It will be easy to use by growers enabling them to incorporate their own informal growing knowledge and to create actual growing control strategy.

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Singh, Rambir, Asheesh K. Singh, and Rakesh K. Arya. "Approximated Simplest Fuzzy Logic Controlled Shunt Active Power Filter for Current Harmonic Mitigation." International Journal of Fuzzy System Applications 1, no. 4 (October 2011): 18–36. http://dx.doi.org/10.4018/ijfsa.2011100102.

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This paper examines the size reduction of the fuzzy rule base without compromising the control characteristics of a fuzzy logic controller (FLC). A 49-rule FLC is approximated by a 4-rule simplest FLC using compensating factors. This approximated 4-rule FLC is implemented to control the shunt active power filter (APF), which is used for harmonic mitigation in source current. The proposed control methodology is less complex and computationally efficient due to significant reduction in the size of rule base. As a result, computational time and memory requirement are also reduced significantly. The control performance and harmonic compensation capability of proposed approximated 4-rule FLC based shunt APF is compared with the conventional PI controller and 49-rule FLC under randomly varying nonlinear loads. The simulation results presented under transient and steady state conditions show that dynamic performance of approximated simplest FLC is better than conventional PI controller and comparable with 49-rule FLC, while maintaining harmonic compensation within limits. Due to its effectiveness and reduced complexity, the proposed approximation methodology emerges out to be a suitable alternative for large rule FLC.

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Nguyen, Nhu, Hung T. Nguyen, Berlin Wu, and Vladik Kreinovich. "Chu Spaces: Towards New Foundations for Fuzzy Logic and Fuzzy Control, with Applications to Information Flow on the World Wide Web." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 3 (May 20, 2001): 149–56. http://dx.doi.org/10.20965/jaciii.2001.p0149.

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We show that Chu spaces, a new formalism used to describe parallelism and information flow, provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations, of membership functions, of defuzzification, etc.

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Pushparajesh V. and Narayana Swamy Ramaiah. "Artificial Intelligent Controller-Based Speed Control of Switched Reluctance Motor." International Journal of Organizational and Collective Intelligence 11, no. 3 (July 2021): 1–13. http://dx.doi.org/10.4018/ijoci.2021070101.

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A new control methodology for controlling the speed of switched reluctance motor (SRM) drive using an intelligent controller is proposed in this paper. The control technology consists of an outer loop fuzzy controller as a speed controller and hysteresis current controller as the inner control loop along with control of switching angles for the four-phase, 8/6 SRM. In this proposed method, the speed control is optimized using the randomly determined fuzzy parameters. Fuzzy interfaced speed control of SRM is simulated using MATLAB/SIMULINK software. The robust performance of the fuzzy logic controller is valued using the least combinations (matrix) of rules for wide ranges of speed and is compared with the proportional-integral (PI) controller. Simulation results reveal that fuzzy-based speed controller gives enhanced performance in the form of quick speed response varies between 0.02sec to 0.12 sec over an extensive range of speed thereby improving the dynamic efficiency of the SRM drive.

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Unal, Gulay. "Integrated design of fault-tolerant control for flight control systems using observer and fuzzy logic." Aircraft Engineering and Aerospace Technology 93, no. 4 (June 4, 2021): 723–32. http://dx.doi.org/10.1108/aeat-12-2020-0293.

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Purpose Fault detection, isolation and reconfiguration of the flight control system is an important problem to obtain healthy flight. This paper aims to propose an integrated approach for aircraft fault-tolerant control. Design/methodology/approach The integrated structure includes a Kalman filter to obtain without noise, a full order observer for sensor fault detection, a GOS (generalized observer scheme) for sensor fault isolation and a fuzzy controller to reconfigure of the healthy sensor. This combination is simulated using the state space model of a lateral flight control system in case of disturbance and under sensor fault scenario. Findings Using a dedicated observer scheme, the detection and time of sensor fault are correct, but the sensor fault isolation is evaluated incorrectly while the faulty sensor is isolated correctly using GOS. The simulation results show that the suggested approach works affectively for sensor faults with disturbance. Originality/value This paper proposes an integrated approach for aircraft fault-tolerant control. Under this framework, three units are designed, one is Kalman filter for filtering and the other is GOS for sensor fault isolation and another is fuzzy logic for reconfiguration. An integrated approach is sensitive to faults that have disturbances. The simulation results show the proposed integrated approach can be used for any linear system.

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Toffano, Zeno, and François Dubois. "Quantum eigenlogic observables applied to the study of fuzzy behaviour of Braitenberg vehicle quantum robots." Kybernetes 48, no. 10 (November 4, 2019): 2307–24. http://dx.doi.org/10.1108/k-11-2018-0603.

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Purpose The purpose of this paper is to apply the quantum “eigenlogic” formulation to behavioural analysis. Agents, represented by Braitenberg vehicles, are investigated in the context of the quantum robot paradigm. The agents are processed through quantum logical gates with fuzzy and multivalued inputs; this permits to enlarge the behavioural possibilities and the associated decisions for these simple vehicles. Design/methodology/approach In eigenlogic, the eigenvalues of the observables are the truth values and the associated eigenvectors are the logical interpretations of the propositional system. Logical observables belong to families of commuting observables for binary logic and many-valued logic. By extension, a fuzzy logic interpretation is proposed by using vectors outside the eigensystem of the logical connective observables. The fuzzy membership function is calculated by the quantum mean value (Born rule) of the logical projection operators and is associated to a quantum probability. The methodology of this paper is based on quantum measurement theory. Findings Fuzziness arises naturally when considering systems described by state vectors not in the considered logical eigensystem. These states correspond to incompatible and complementary systems outside the realm of classical logic. Considering these states allows the detection of new Braitenberg vehicle behaviours related to identified emotions; these are linked to quantum-like effects. Research limitations/implications The method does not deal at this stage with first-order logic and is limited to different families of commuting logical observables. An extension to families of logical non-commuting operators associated to predicate quantifiers could profit of the “quantum advantage” due to effects such as superposition, parallelism, non-commutativity and entanglement. This direction of research has a variety of applications, including robotics. Practical implications The goal of this research is to show the multiplicity of behaviours obtained by using fuzzy logic along with quantum logical gates in the control of simple Braitenberg vehicle agents. By changing and combining different quantum control gates, one can tune small changes in the vehicle’s behaviour and hence get specific features around the main basic robot’s emotions. Originality/value New mathematical formulation for propositional logic based on linear algebra. This methodology demonstrates the potentiality of this formalism for behavioural agent models (quantum robots).

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Paz, Alexander, Pankaj Maheshwari, Pushkin Kachroo, and Sajjad Ahmad. "Estimation of Performance Indices for the Planning of Sustainable Transportation Systems." Advances in Fuzzy Systems 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/601468.

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In the context of sustainable transportation systems, previous studies have either focused only on the transportation system or have not used a methodology that enables the treatment of incomplete, vague, and qualitative information associated with the available data. This study proposes a system of systems (SOS) and a fuzzy logic modeling approach. The SOS includes the Transportation, Activity, and Environment systems. The fuzzy logic modeling approach enables the treatment of the vagueness associated with some of the relevant data. Performance Indices (PIs) are computed for each system using a number of performance measures. The PIs illustrate the aggregated performance of each system as well as the interactions among them. The proposed methodology also enables the estimation of a Composite Sustainability Index to summarize the aggregated performance of the overall SOS. Existing data was used to analyze sustainability in the entire United States. The results showed that the Transportation and Activity systems follow a positive trend, with similar periods of growth and contractions; in contrast, the environmental system follows a reverse pattern. The results are intuitive and are associated with a series of historic events, such as depressions in the economy as well as policy changes and regulations.

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Indahingwati, Asmara, Muh Barid Nizarudin Wajdi, Dwi Ermayanti Susilo, Nuning Kurniasih, and Robbi Rahim. "Comparison Analysis of TOPSIS and Fuzzy Logic Methods On Fertilizer Selection." International Journal of Engineering & Technology 7, no. 2.3 (March 8, 2018): 109. http://dx.doi.org/10.14419/ijet.v7i2.3.12630.

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Decision Support System is an interactive system that supports decision in the decision-making process through alternatives derived from the processing of data, information and design of the models. Selection decision support system of chemical fertilizer in fruit plant is expected to help anyone who wants to cultivate fruit trees can determine the chemical fertilizer as expected based alternatives and criteria set by the user. In this research method used is TOPSIS Method and Method of Fuzzy Logic. TOPSIS method is one of multiple criteria decision making method that uses the principle that the alternatives selected must have the shortest distance. Fuzzy Logic is a methodology of control systems troubleshooting, the fuzzy logic stated that everything is a binary which means it is only two possibilities, "Yes or No", "True or False", "Good or Bad", and others. Therefore, all of these can have a membership value of 0 or 1.

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Journal articles on the topic 'Control methodology][Fuzzy logic'

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