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Feeding fish to fulfill the nutritional needs of the fish can increase the growth of fish to a marketable size. However, manual feeding requires a lot of time and money. Therefore, an automation system is needed that can help feed fish automatically at the right time and in the right amount. To overcome this problem, an Unmanned Surface Vehicle (USV) Fish Feeder product with fuzzy logic control is an interesting topic to research which has the advantage of being able to reduce production costs and the time required to feed manually. The navigation system must be able to move according to the specified destination. In this research, a navigation system based on waypoint using GPS and compass is designed. A fuzzy logic control system will be applied to control the movement of the USV Fish Feeder with input distance error and angle error. The results of the tests that have been carried out are that the navigation system designed can move the USV Fish Feeder to a predetermined waypoint. The error results on the autonomous system with fuzzy logic controller are 1.38 meters with an accuracy of 92.78% in the first test, 1.7 meters with an accuracy of 91.1% in the second test, 1.3 meters with an accuracy of 94.79% in the third test, and 0.7703 meters with an accuracy of 95.83% in the fourth test as the best result. [ABSTRACT FROM AUTHOR]

For urban houseplant enthusiasts with limited space, indoor vertical farming (IVF) is a solution. Not only does it save space, but it also makes monitoring easier. Many artificial conditions must be added to match the desired growth and development conditions of plants. This study is part of agricultural IVF research for precision farming systems. As part of this research, an intelligent pest control concept was developed using a fuzzy logic control system. Fuzzy logic control systems are designed based on environmental preferences. The influencing factors are pest abundance, temperature, and relative humidity, resulting in pest control. The use of this automatic pest control system is expected to help farmers care for ornamental plants. As a case study, this study used the control of the pest leafhopper on the growth of ornamental azalea plants. These results demonstrate the feasibility of an in vitro fertilization system using fuzzy logic, and it is expected that further research applying this logic will be conducted in the future. From the simulation results, it can be concluded that this FIS (fuzzy inference system) is suitable and can be used for further development, such as pesticide spraying according to plant conditions. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent robots, the requirements for robot response speed and control accuracy continue to rise. Field-Oriented Control is widely used to control the joint motors of robots. It is also called vector control. Compared with the six-step commutation method, it can control the torque and speed of the motor more accurately. In Field-Oriented Control, traditional PID control plays a feedback role. It can adjust the current value to control the torque and speed to ensure the stability of the system. However, in real life, when the robot is in different motion states, its working status is different. Traditional PID control has poor adaptability and robustness to changes. Therefore, multi-modal control is required for the motor control of the robot. In this paper, MATLAB is used to optimize traditional PID control, and fuzzy logic algorithms are added to make the system highly robust and adaptive. The simulation results show that the FOC control system based on fuzzy PID control has faster response speed, higher accuracy, and better robustness than the general FOC control system. As the demand for automation and intelligence continues to rise in the future, the FOC control system based on fuzzy PID control has higher precision, good stability, and adaptability, which can improve the control of industrial robots, autonomous driving, and other fields. [ABSTRACT FROM AUTHOR]

This article provides a synthesis method for intelligent control systems based on a Takagi-Sugeno fuzzy model structure. It shows construction of a Takagi-Sugeno fuzzy controller using a "parallel-distributed correction" concept, which is based on introduction of a fuzzy process model into a feed-back channel. The simulation of an intelligent control system, as well as a system using classical approaches, was carried out. The dynamic characteristics of classical and intelligent control systems are investigated. The introduction of this approach allows you to get complete information about the control object, both in quantitative and qualitative form. Such an approach helps to obtain complete information about a control object both in quantitative and qualitative form. This design practice is conceptually simple, natural, and can be explained in the context of a rotary drive control system of an industrial robot. [ABSTRACT FROM AUTHOR]

Synthesizing and researching of an intelligent control systems for unmanned aerial vehicles (UAVs) is at the core of the autonomous performance of their missions and tasks. In the presented material, the object of the control is studied. It is a UAV with suitable construction and aerodynamic body composition. The performing research under chosen UAV is for static and dynamic stability of it. UAV aerodynamic coefficients of the forces and the moments are obtained in the software environment of a virtual tunnel. A mathematical state-space model for longitudinal motion is synthesised with the results from the virtual tunnel. The closed-loop system in the MATLAB-Simulink environment of the UAV is modelled and a fuzzy logic controller is synthesised to control the pitch angle in function of the elevator's angle deflection of the aircraft. Linguistic rules are developed, which are the basis of operation of the fuzzy controller. This controller is used to control the longitudinal motion of the UAV. [ABSTRACT FROM AUTHOR]

The article demonstrates a method of intelligent control of a complex intersection using a fuzzy controller optimized by a genetic algorithm. A series of experiments on optimization of fuzzy controllers with from 2 to 13 rules sets has been performed. Based on the simulation results, it was assumed that there is no linear dependence of the model management efficiency indicator on the number of rules in the set. It is shown that the most suitable for control is a package of 12 rules, which has the potential for further optimization. [ABSTRACT FROM AUTHOR]

In this paper we discuss the research about genetic algorithm application in fuzzy controller synthesis. Genetic algorithm is artificial intelligence approach and it allows finding fuzzy controller parameters which can provide the extreme value of optimization criterion. The authors describe the work of variation operator which includes into genetic algorithm. This research presents the results of automatic control system modeling simulation, input variables term-sets, rule base, array of fuzzy controller parameters and distribution of squared variances sum for variation of different groups of fuzzy controller parameters. Also article contains statistical analysis by testing statistical hypothesis about significant difference between variation of membership function parameters, rules consequences and rule weights. [ABSTRACT FROM AUTHOR]

When there is full uncertainty about a problem, fuzzy expert system is an important tool for modeling human thinking and solving. FES uses fuzzy sets and fuzzy logic to represent various types of information about the facts of the situation, as well as to model the interactions and relationships that exist among variables. The purpose of this work is to construct the FES i n order to investigate the influence of mosquito control and population reduction. Mosquito control is the process of controlling mos quito populations in order to reduce the problems they cause to people's health, economy, and enjoyment. Mosquito management is an important public-health activity all around the world because mosquitoes spread a number of diseases like malaria, dengue fever and the Zika virus. The novelty of this current research is to model a fuzzy expert system to control mosquito population with the help of experts. [ABSTRACT FROM AUTHOR]

Control systems are one of the best ways to reduce damage to structures against natural disasters such as earthquakes, which has received special attention from researchers. In this regard, the role and effectiveness of the application of semi-active control systems due to high performance in controlling structures and maintaining their stability without the need for a large energy source has been considered. What is important is to increase the efficiency of these systems with the help of modification methods and provide appropriate algorithms that can manage the behavior of these systems. Previous studies have shown that the use of dampers in construction frames is limited by their performance. In this research, the enhancement of MR damper performance in the seismic control of frame-shaped structures was studied using a modification mechanism. To this aim, a three-story benchmark shear frame structure was considered under the modification mechanism. The performance of the control system was evaluated under Electro earthquake record. In determining the seismic responses of a structure, the motion equations of a building equipped with an MR damper are studied and solved. By changing the input voltage, the MR attenuator produced the damping force to reduce the responses of the structure. The results indicated that by retrofitting the structure with an MR damper using the PSO algorithm, the displacement responses of the corner of the roof of the structure have decreased by 91.75 percent compared to the uncontrolled mode. Regarding the fact that one of the most important issues in natural systems is the issue of uncertainty and fuzziness, in this research, by using fuzzy inference and optimizing using the PSO algorithm along with MR dampers, the system was controlled and improved. Furthermore, the results were better than the similar case that used the genetic algorithm, the damping time was shorter, and the damping rate was higher. Based on this, the difference between using the PSO algorithm and genetic algorithm in reducing roof corner displacement is 69.61 percent. [ABSTRACT FROM AUTHOR]

One of the high-level risks of work accidents in the oil and gas industry is the separation process of the oil phase after the drilling process. Crude oil, which is flammable, become very dangerous to the field workers who are tasked to monitor the separation process. To monitor and control the oil separation process safely, IoT has become the solution. Furthermore, IoT can be a solution to reduce operational and maintenance costs in the petroleum exploration process. Research has been carried out on the implementation of IIoT-based control and monitor systems and optimization of level control systems using fuzzy PID controllers on a three-phase separator mini plant. The results showed the Industrial Wireless Sensor and Actuator Networks (IWSAN) communication system using the Zigbee IEEE 802.15.4 protocol at the mini plant communicated in real-time at a distance of 100m with a delay time of 16ms. The PID-based fuzzy level control system can produce a better dynamic response than PI and PID control systems. The mixed level control shows a maximum overshoot of 2%, a settling time of 587 seconds, a steady-state error of 1%, a rise time of 466 seconds, and IAE 2546. Meanwhile, the control of an oil level shows a maximum overshoot of 3%, a settling time of 362 seconds, a steady-state error of 1 %, a rise time of 226 seconds, and IAE 1429.4. [ABSTRACT FROM AUTHOR]

The world is actively concerned about renewable energy to solve the global warming issue, especially in Indonesia. Indonesia had set a target of 23% renewable energy in 2025 but still reached 11,20% in 2020. Indonesia has relatively high renewable energy sources but has not utilized them optimally. Indonesia's renewable energy was dominated by hydro, geothermal, biomass, and solar, while the use of wind energy has a challenge, considering that Indonesia is located in equatorial region, with unstable wind power. Combining two or more renewable energy sources with a battery-connected dc-dc converter with multiple inputs could be an alternative solution to overcome the weakness of a single renewable energy source. This research aims to implement fuzzy logic control on the output voltage of the multi-input buck converter. The results of the output control system on the multi-input buck converter shows that the average error value is 0,54% and the highest error value is 3,68% at 13,87 V. [ABSTRACT FROM AUTHOR]

The paper dwells upon a simulation of a sophisticated intelligent control system with two-zone speed regulation using a fuzzy set theory. It is shown that the approach suggested can be applied not only to regulate by modules configured to change a single coordinate but also effectively apply complex control procedures of internal elements that combine the settings to regulate different physical quantities. It introduces a model of the multi-stage fuzzy control system pointed to rate control in the modes 'up to nominal' and 'over nominal'. The control systems constructed with the applied common methods, conventional approaches based on the fuzzy set theory, and developed intelligent systems with the proposed algorithm used are reviewed and their dynamic characteristics are studied. Further development of the suggested technique will allow the implementation of operating systems of such an object class not only in dual-zone control modes but also to compensate a significant amount of non-linear elements characterizing such an object. [ABSTRACT FROM AUTHOR]

This article deals with the determination of a state feedback control for a fuzzy system of type TSK. The use of a special transformation allows us to determine the vector of feedback gain with a simple and effective method. The obtained result made easier the determination of the system parameters guaranteeing the condition of stability of the studied system. As well, the new state-space representation facilitates us to make up a link between the parameters selected arbitrarily and the feedback gain guarantees stability. [ABSTRACT FROM AUTHOR]

Nowadays the automobile industry is prosperous with the evolution of Electric Vehicle (EV). Battery charging is more concern for the development of electric vehicle. In the distribution system this proposes a communication free charging controller is used in this proposed work, using this controller to find the battery is getting charging or discharging during sunshine. In charging station while using solar energy sometimes of power quality issues will occur and we can rectify that power quality issues in electric vehicle. SEPIC (Single Ended Primary Inductor Converter) converter as well as Bidirectional DC-DC converter used to able of charging the EV battery in sunshine and non sunshine hours. The performances of the proposed method are evaluated through simulations using MATLAB/ simulink software. [ABSTRACT FROM AUTHOR]

This present study proposes a current controlled modulation method for brushless DC motors for electric vehicle. A quasi-square current control technique is used. Conventional controllers such as PI, PID controllers are used first, then the system is evaluated using Fuzzy Logic controller which further improves the system and also fuzzy controller is far better than other controllers due to the use of linguistic terms. [ABSTRACT FROM AUTHOR]

In this paper, was considered the process of the Mamdani type fuzzy controllers operation with such defuzzification methods as the center of gravity method, the center of area method, and the methods of left and right modal values. The fuzzy controller was used to control the dynamic stabilization of the inverted pendulum unstable equilibrium position. Graphical models of term sets for the angle of deviation from the vertical, the horizontal displacement of the carriage and the control action on the carriage, which were used in the fuzzy controller operation, are illustrated. The rule base for the considered fuzzy controller is shown. The values of the control parameters errors integrals sum for automatic control systems with fuzzy controllers that differ in the defuzzification methods are calculated. Graphs of the change in the angle of deviation from the vertical are constructed, showing the achievement of inverted pendulum unstable equilibrium dynamic stabilization. [ABSTRACT FROM AUTHOR]

This paper presents a method of Fault Tolerant Control (FTC) for vehicle lateral dynamics described by Takagi-Sugeno (T-S) fuzzy model to approximate the vehicle lateral dynamics with consideration of the roll motion. This approach focuses on actuator faults; which requires the knowledge of the system parameters and the occurring faults. For that, a Proportional-Integral Observer (PIO) is adapted to estimate the system states and faults. The proposed control makes it possible to preserve the stability of the vehicle even in the presence of these faults. The observer and controller designs are based on the Lyapunov approach formulated in Linear Matrix Inequalities (LMIs). The simulation results clearly show a robustness of the proposed observer and a rigorous stability of the proposed controller strategy to maintain a stability of the vehicle. [ABSTRACT FROM AUTHOR]

Problem of fuzzy transportation efforts to lessen price of transportation of wares all the way through an enable capacitate system. In this article, a new approach using hexagonal fuzzy numbers in transportation problem with fuzzy environment is discussed. In the projected algorithm transportation price, accessibility and demand of the merchandise are signified by hexagonal fuzzy numbers. The main aim of this paper is to introduce a new method for solving hexagonal fuzzy transportation problem (FTP) on the basis of ranking method of fuzzy numbers (FNs). The proposed method is demonstrated with a numerical example and compared with existing method in literature and it is found in good compliance. [ABSTRACT FROM AUTHOR]

The article discusses the automatic control system of the Diamond DA42 aircraft anti-icing system. The project was created in MATLAB environment equipped with the Fuzzy Logic Toolbox package. The discussion revolves around the problem of preventing icing. In the analysis four parameters were taken into account: air temperature, cloud water content, temperature of the aircraft and precipitation. On the basis of the initial parameters, ice intensity likely to occur on the air-craft was determined. By automating the activities related to the programming of the anti-icing system, it is possible to significantly relieve the pilot and increase the safety of air operations, particularly in conditions which are conducive to the formation of icing. Currently, the Diamond DA42 aircraft is not authorized to fly into known icing conditions. The use of a fuzzy expert system to control the anti-icing system would greatly increase the scope of operations of the Diamond DA42 aircraft. [ABSTRACT FROM AUTHOR]

The main aim of this work was to create a model and carry out simulation tests of a hydraulic system for generating a pressure pulse with strictly defined time parameters. Obtaining a proper pressure course is a key condition for correctness of a long-term testing of hydraulic components durability, including e.g. new types of hoses. The requirements regarding the shape of the impulse were formulated on the basis of the ISO 6803:2008 standard which concerns hydraulic pressure testing of rubber hoses and hose assemblies without flexing. In the first step, a mathematical model of the system was built using equations describing the system geometry, flow equations and flow balances formulated for the separated volumes. Next, a simulation model of the hydraulic system was created in Matlab-Simulink. After obtaining flow characteristics, a model of a required pressure signal generator was built, followed by a fuzzy logic controller created in the Anfis editor of the Fuzzy Logic Toolbox. During the iterative tuning process, parameters of the fuzzy controller were adjusted. Then, the curves of the obtained and the required pressure for different values of the flow rate of the working medium were compared. As arises from the comparison results, the fuzzy logic controller fulfills the requirements and thus may be an advantageous option for generating the pressure signal. [ABSTRACT FROM AUTHOR]

The research establishes a Takagi-Sugeno Fuzzy controller to control a desired tip angle position of a rotary flexible joint robotic arm. The TSF is also employed to dampen the vibration that produced when a rotary flexible joint robotic arm reaching to a desired tip angle position. In order to assess the controller performance, the proposed TSF is compared with the Linear Quadratic Regulator (LQR) controller via simulation and experiment. In particular, there are two different angles affect the system performance that need to be controlled namely the servo angle, 9, and the arm deflection, a. The results showed that the TSF is satisfactorily controlled both the servo angle, 9 and the arm deflection, a. In addition, the TSF performed better than LQR controller in simulation and experiment. [ABSTRACT FROM AUTHOR]

Control Charts sometimes indicate out of control conditions that are in fact in control. In standard Control Charts, control limits and observations are taken as crisp numbers while in NAFE charts, limits and observations are non asymptotic fuzzy estimators. In standard charts, if a sample exceeds a limit by 0,001 it is an out of control condition. In NAFE Charts, the ratio of exceeding a control limit is taken into account which results to a smaller false alarm rate. NAFE can also be used in short run cases were the false alarm rate is critical for the evaluation. Production manager can decide if the sample is out of control, judging by how strict quality control is necessary. Thus, charts that lead to more trustable conclusions are produced. [ABSTRACT FROM AUTHOR]

The Greater Jakarta Area is an urban area consisting of the capital city of Indonesia - Jakarta and the buffer region that have been continually growing rapidly. Traffic demand of the road network from and toward the core of the Greater Jakarta area has been and will continue to expand. Fortunately, the incremental level is declined. Comparing previous traffic survey in The Greater Jakarta Area, traffic volume in 1988-1993 has increased by 12.6% per year, and from 1993 to 2000 has increased by 6% per year [3]. However, the traffic increase is very significant, and potentially significant in creating severe traffic congestion in The Greater Jakarta Area in the future. The Greater Jakarta Area has a lot of plans to build a mass transit system, such as the development of bus rapid transit (BRT), Mass Rapid Transit (MRT), monorail and commuter rail revitalization, etc. This research is expected to give special attention to build a tool in the decision-making method to assess the priority of an investment plan in a mass transport strategic plan in developing countries. The developed method is Modified Fuzzy Multi Method Actor - Multi Level Decision Making, which is trying to be developed by the authors. The survey was conducted in The Greater Jakarta Area involving respondents from the involved actors, i.e., public transit user, society (non-users), operators and investors and the government as regulator. In addition, data collected from various levels of stakeholders from various levels of authority viz. the district/city, provincial and central government level. The results of this research show that the decision-making process is more effective, objective and more comprehensive. [ABSTRACT FROM AUTHOR]

Surface and their description plays an important role in design, physical, geology and other natural phenomenon. In this paper, intuitionistic fuzzy Bézier surface approximation that considers bicubic case with 4× 4 control net is introduced. The intuitionistic fuzzy control net relation is defined through intuitionistic fuzzy concept. Later, the control net is blended with Bernstein basis function to obtain surface blending function and to produce intuitionistic fuzzy bicubic Bézier surface. Lastly, the intuitionistic fuzzy bicubic Bézier surface is illustrated through the approximation method by using data point with intuitionistic features. [ABSTRACT FROM AUTHOR]

In this paper, the cubic Bézier curve interpolation method for intuitionistic fuzzy data is introduced based on the theory of intuitionistic fuzzy sets. The intuitionistic fuzzy point relation and intuitionistic fuzzy control point relation is defined based on intuitionistic fuzzy point, intuitionistic fuzzy number and intuitionistic fuzzy relation concepts. Next, the intuitionistic fuzzy control point relation is blended with the Bernstein basis function and the intuitionistic fuzzy cubic Bézier curve is produced. Then, the intuitionistic fuzzy data is interpolated and visualized by using the intuitionistic fuzzy cubic Bézier curve. [ABSTRACT FROM AUTHOR]

The software tool namely Confidence Interval Calculator (CIC) presented in this paper is designed to compute confidential interval of fuzzy random regression (FRR) model. FRR model uses fuzzy random input-output data to construct confidential interval for prediction. Manual calculation by hand of FRR however requires lengthy time to compute the result. Hence, CIC is introduced to alleviate computational difficulties and to reduce the computational time. The tool provides a function to capture input (fuzzy random) data and to calculate the expected value, and the variance. Technical formulation is embedded in the tool which enables the tool to compute the result correctly. The solution of a technical problem is described with numerical example. [ABSTRACT FROM AUTHOR]

In order to improve the frequency control performance of the interconnected power grid, overcome the problems of poor robustness and slow adjustment of traditional regulation, the paper puts forward a frequency control method based on fuzzy PID. The method takes the frequency deviation and tie-line deviation of each area as the control objective, takes the regional frequency deviation and its deviation as input, and uses fuzzy mathematics theory .adjusts PID control parameters online. By establishing the regional frequency control model of water-fire complementary power generation in MATLAB. the regional frequency control strategy is given, and three control modes (TBC-FTC. FTC-FTC. FFC-FTC) are simulated and analyzed. The simulation and experimental results show that, this method has better control performance compared with the traditional regional frequency regulation. [ABSTRACT FROM AUTHOR]

Customer positioning has always been a problem that enterprises focus on. In this paper, FCM clustering algorithm is used to cluster customer groups. However, due to the traditional FCM clustering algorithm, which is susceptible to the influence of the initial clustering center and easy to fall into the local optimal problem, the short board of FCM is solved by the gray optimization algorithm (GWO) to achieve efficient and accurate handling of a large number of retailer data. [ABSTRACT FROM AUTHOR]

In this paper a Particle Swarm Optimization (PSO) algorithm is considered in order to optimize the triangular Membership Functions (MF) in a Fuzzy Logic Controller (FLC). PSO algorithm belongs to the class of Swarm Intelligence (SI) techniques and is considered an efficient heuristic technique for optimization problem in a continuous and multidimensional search spaces. Performance of a FLC depends on the fuzzy partition of each input/output space considered and the PSO algorithm can be used to obtain the optimal or near optimal parameters of the triangular membership functions in order to achieve the best results in the defuzzification process. Simulation results obtained by this approach to tune the triangular membership functions of a FLC for an application concerning the optimization of the energy consumption in Industrial Wireless Sensor Networks (IWSN) are reported. [ABSTRACT FROM AUTHOR]

A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop. [ABSTRACT FROM AUTHOR]

Focused on the circumstance that the equipment using demand in the short term and the development demand in the long term should be made overall plans and took into consideration in the weapons portfolio planning and the practical problem of the fuzziness in the definition of equipment capacity demand. The expression of demand is assumed to be an interval number or a discrete number. With the analysis method of epoch-era, a long planning cycle is broke into several short planning cycles with different demand value. The multi-stage stochastic programming model is built aimed at maximize long-term planning cycle demand under the constraint of budget, equipment development time and short planning cycle demand. The scenario tree is used to discretize the interval value of the demand, and genetic algorithm is designed to solve the problem. At last, a case is studied to demonstrate the feasibility and effectiveness of the proposed mode. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the further properties of fuzzy metric type spaces introduced by Saadati [1]. We prove that a M-convergent sequence in a fuzzy metric type space has a unique limit. Later we show that for a fuzzy metric type space (X, M, *, K) if M is continuous with respect to the first (second) variable then M is continuous with respect to the second (first) variable and M-convergence are equivalent to convergence. Moreover, in such spaces open balls are open and closed balls are closed, therefore induced topology is Hausdorff, first countable, regular and metrizable. [ABSTRACT FROM AUTHOR]

Clustering aims to classify the different patterns into groups called clusters. In this clustering method, we use n-mers frequency to calculate the distance matrix which is considered more accurate than using the DNA alignment. The clustering results could be used to discover biologically important sub-sections and groups of genes. Many clustering methods have been developed, while hard clustering methods considered less accurate than fuzzy clustering methods, especially if it is used for outliers data. Among fuzzy clustering methods, fuzzy c-means is one the best known for its accuracy and simplicity. Fuzzy c-means clustering uses membership function variable, which refers to how likely the data could be members into a cluster. Fuzzy c-means clustering works using the principle of minimizing the objective function. Parameters of membership function in fuzzy are used as a weighting factor which is also called the fuzzier. In this study we implement hybrid clustering using fuzzy c-means and divisive algorithm which could improve the accuracy of cluster membership compare to traditional partitional approach only. In this study fuzzy c-means is used in the first step to find partition results. Furthermore divisive algorithms will run on the second step to find sub-clusters and dendogram of phylogenetic tree. To find the best number of clusters is determined using the minimum value of Davies Bouldin Index (DBI) of the cluster results. In this research, the results show that the methods introduced in this paper is better than other partitioning methods. Finally, we found 3 clusters with DBI value of 1.126628 at first step of clustering. Moreover, DBI values after implementing the second step of clustering are always producing smaller IDB values compare to the results of using first step clustering only. This condition indicates that the hybrid approach in this study produce better performance of the cluster results, in term its DBI values. [ABSTRACT FROM AUTHOR]

In this paper, fuzzy-PID adaptive control method is applied to the control of tractor electric-hydraulic hitch system. According to the characteristics of the system, a fuzzy-PID adaptive controller is designed and the electrichydraulic hitch system model is established. Traction control and position control performance simulation are carried out with the common PID control method. A field test rig was set up to test the electric-hydraulic hitch system. The test results showed that, after the fuzzy-PID adaptive control is adopted, when the tillage depth steps from 0.1m to 0.3m, the system transition process time is 4s, without overshoot, and when the tractive force steps from 3000N to 7000N, the system transition process time is 5s, the system overshoot is 25%. [ABSTRACT FROM AUTHOR]

Aiming at the steering wheel differential steering control technology of rear wheel independent driving electric wheel, considering the assisting effect of electronic differential control on vehicle steering, based on the high speed steering characteristic of electric wheel car, the electronic differential speed of auxiliary wheel steering is also studied. A yaw moment control strategy is applied to the vehicle at high speed. Based on the vehicle stability reference value, yaw rate is used to design the fuzzy controller to distribute the driving wheel torque. The simulation results show that the basic electronic differential speed function is realized based on the yaw moment control strategy, while the vehicle stability control is improved and the driving safety is enhanced. On the other hand, the torque control strategy can also assist steering of vehicle. [ABSTRACT FROM AUTHOR]

Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. Various methods for maximum power point tracking (MPPT) were developed and finally implemented in solar power electronic controllers to increase the efficiency in the electricity production originate from renewables. In this paper we compare using Matlab tools Simulink, two different MPP tracking methods, which are, fuzzy logic control (FL) and sliding mode control (SMC), considering their efficiency in solar energy production. [ABSTRACT FROM AUTHOR]

In the present paper, an optimal operation of a grid-connected variable speed wind turbine equipped with a Doubly Fed Induction Generator (DFIG) is presented. The proposed cascaded nonlinear controller is designed to perform two main objectives. In the outer loop, a maximum power point tracking (MPPT) algorithm based on fuzzy logic theory is designed to permanently extract the optimal aerodynamic energy, whereas in the inner loop, a second order sliding mode control (2-SM) is applied to achieve smooth regulation of both stator active and reactive powers quantities. The obtained simulation results show a permanent track of the MPP point regardless of the turbine power-speed slope moreover the proposed sliding mode control strategy presents attractive features such as chattering-free, compared to the conventional first order sliding technique (1-SM). [ABSTRACT FROM AUTHOR]

In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved. [ABSTRACT FROM AUTHOR]

Traditional filed oriented control strategy including proportional-integral (PI) regulator for the speed drive of the doubly fed induction motor (DFIM) have some drawbacks such as parameter tuning complications, mediocre dynamic performances and reduced robustness. Therefore, based on the analysis of the mathematical model of a DFIM supplied by two five-level SVPWM inverters, this paper proposes a new robust control scheme based on super twisting sliding mode and fuzzy logic. The conventional sliding mode control (SMC) has vast chattering effect on the electromagnetic torque developed by the DFIM. In order to resolve this problem, a second order sliding mode technique based on super twisting algorithm and fuzzy logic functions is employed. The validity of the employed approach was tested by using Matlab/Simulink software. Interesting simulation results were obtained and remarkable advantages of the proposed control scheme were exposed including simple design of the control system, reduced chattering as well as the other advantages. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are characterized by a distributed architecture realized by a set of autonomous electronic devices able to sense data from the surrounding environment and to communicate among them. These devices are battery powered since they may be used even to monitor hazardous events in inaccessible areas. As a consequence, it is preferable to assure the adoption of energy management solutions in order to extend the WSN lifetime, as far as possible. Moreover, it is crucial to guarantee that the nodes receive the transmitted data correctly. It is clear that trading off power optimization and quality of service has become one the most important concerns when dealing with modern systems based on WSNs. This paper introduces a solution based on a Fuzzy Logic Controller (FLC) focusing on the minimization of energy consumption of wireless sensor nodes. This is made possible because the sleeping time of these nodes is dynamically regulated by a FLC. [ABSTRACT FROM AUTHOR]

The ovarian system exhibits nonlinear dynamics which is modeled by a set of coupled nonlinear differential equations. The paper proposes adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be described as differential functions of the flat output. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments. [ABSTRACT FROM AUTHOR]

It is shown that the model of the hypothalamic-pituitary-adrenal gland axis is a differentially flat one and this permits to transform it to the so-called linear canonical form. For the new description of the system's dynamics the transformed control inputs contain unknown terms which depend on the system's parameters. To identify these terms an adaptive fuzzy approximator is used in the control loop. Thus an adaptive fuzzy control scheme is implemented in which the unknown or unmodeled system dynamics is approximated by neurofuzzy networks and next this information is used by a feedback controller that makes the state variables (CRH - corticotropin releasing hormone, adenocortocotropic hormone - ACTH, cortisol) of the hypothalamic-pituitary-adrenal gland axis model converge to the desirable levels (setpoints). This adaptive control scheme is exclusively implemented with the use of output feedback, while the state vector elements which are not directly measured are estimated with the use of a state observer that operates in the control loop. The learning rate of the adaptive fuzzy system is suitably computed from Lyapunov analysis, so as to assure that both the learning procedure for the unknown system's parameters, the dynamics of the observer and the dynamics of the control loop will remain stable. The performed Lyapunov stability analysis depends on two Riccati equations, one associated with the feedback controller and one associated with the state observer. Finally, it is proven that for the control scheme that comprises the feedback controller, the state observer and the neurofuzzy approximator, an H-infinity tracking performance can be succeeded. [ABSTRACT FROM AUTHOR]

Vehicle dynamics plant phenomena during acceleration, deceleration and curve turning in uncertainty ground road is very complexity and nonlinearity model. No integrated chassis control by various systems have been proposed to control vehicle behavior by distributing front-rear and left-right braking/ABS & driving/TCS forces, adjusting rear-wheel steering angle/4WS. ABS and TCS can reduce the slip and skid factor that improve the braking and driving performance but cannot eliminate the vehicle sideslip (VSS). In other side four wheel steering (4WS) have good performance in reducing of vehicle sideslip to improve maneuver ability but fail in decreasing of stopping distance (SD) and increasing driving performance (DP). In this research, to solve the previous problem, author purpose an integrated ABS-TCS-4WS control using mamdani-fuzzy logic. Input and output variable are described. Steering maneuver, slope of the road, vehicle mass (number of passenger) are the input variables, while error of wheel slip factor, error of skid factor and error of vehicle sideslip are output variables. In this research, parameter for reference, wheel slip and skid factor are setup to 20% and vehicle sideslip to be zero degree. 22 rules was setup to produce the best mamdani fuzzy in adapting the nonlinearity of the vehicle dynamics. Result of the research shown, that error of wheel slip factor was 3.2%, error of skid factor was 2.8% and error of vehicle side slip was 4.2 degree. [ABSTRACT FROM AUTHOR]

Steam temperature of extraction plays a major role in steam distillation essential oil extraction. The process variable gives large effect to the output yield and quality of the oils. This study proposed a robust hybrid fuzzy combined with PID (HFPID) controller to control the steam temperature in the extraction process. The controller was attached to a hydro-diffusion to overcome the current issue facing by conventional steam distillation. The hydro-diffusion steam distillation plant enhances oil recovery by minimizing oil waste in boiling water. HFPID controller is expected to improve system performance at transient and steady state in order to promises the best quality of essential oil. The temperature control was achieved by controlling the voltage fed to the heater ranging from 0V to 5V via digital-toanalogue converter (DAC). The HFPID controller using 49 rules was compared with the conventional PID controller in term of rise time, settling time, percentage overshoot (%OS) and RMSE. From the result, the real time performance of HFPID controller reveals that this controller is compatible for the system because able to minimize the output error at transient state and gave the faster response with small overshoot, settling time and RMSE. [ABSTRACT FROM AUTHOR]

The problem of pattern recognition has been solved with numerous methods in the Artificial Intelligence field. We present an unconventional method based on Lingustic Fuzzy Logic Forecaster which is primarily used for the task of time series analysis and prediction through logical deduction wtih linguistic variables. This method should be used not only to the time series prediction itself, but also for recognition of patterns in a signal with seasonal component. [ABSTRACT FROM AUTHOR]

Control chart is a technique that has been used widely in industry and services. P chart is the simplest control chart. In this chart, item is classified into two categories as either conforming and non conforming. This chart based on binomial distribution. In practice, each item can classify in more than two categories such as very bad, bad, good and very good. Then to monitor the process we used multinomial p control chart. However, if the classification is an element of vagueness, the fuzzy multinomial control chart (FM) is more appropriately used. Control limit of FM chart obtained multinomial distribution and the degree of membership using fuzzy trianguler are 0, 0.25. 0.5 and 1. This chart will be applied to the data glass and will compare with multinomial p control chart. [ABSTRACT FROM AUTHOR]

In this work the use of a cartesian robotic manipulator as scanner for the automated identification of hidden defects in an aluminum test plate is proposed. The robotic manipulator includes a custom made soft deformable ultrasonic probe and a force sensor for the recollection of the ultrasonic signals and force feedback. The contact between the soft probe and the test plate is regulated using a Neuro Fuzzy controller in order to avoid the complex mathematical model produced by the interaction. Finally the use of the correlation coefficient is proposed for the post processing of the obtained ultrasonic signals and identification of hidden defects inside the test plate. Experimental studies demonstrated the efficiency of the method. [ABSTRACT FROM AUTHOR]

The Power Line Communications (PLC) technology allows the use of the power grid in order to ensure the exchange of data information among devices. This work proposes an approach, based on Fuzzy Logic, that dynamically manages the amplitude of the signal, with which each node transmits, by processing the master-slave link quality measured and the master-slave distance. The main objective of this is to reduce both the impact of communication interferences induced and power consumption. [ABSTRACT FROM AUTHOR]

This paper describes a new control method of the induction motor using the type-2 fuzzy logic. This technique is used to overcome the problem of the sensitivity to the parametric variation of the motor. The vector control technique solves the coupling problem. Unfortunately, in practice this is not checked because of uncertainties in the model. Indeed, the presence of uncertainties led us to use the techniques of fuzzy logic. The performance of this control is evaluated in comparison to the classical vector control. The control algorithm is presented and a set of simulations is carried out in order to prove the good performances of the proposed solution. [ABSTRACT FROM AUTHOR]