Earticle

This paper considers supply chain models with two competitive manufacturers acting as the leaders and a retailer acting as follower under a fuzzy decision environment. The parameters of demand function and manufacturing cost are treated as fuzzy variables. Two manufacturers are assumed to pursue Cournot competitive behavior and the optimum policy of the expected value and chance-constrained programming models are derived. Finally, a numerical example is provided to illustrate the results of proposed models. It is shown that in fuzzy models, the confidence level of the profits for supply chain members affects the final optimal solutions.

This paper considers the fuzzy modeling and robust fuzzy variable control for quadrotor in uncertain environment. The nonlinear system of quadrotor is firstly analyzed in this paper by utilizing laws of motion and force. Then, a Takagi-Sugeno (T-S) fuzzy model is achieved to approximate the system. LMI method is used to acquire an improved sliding surface. On this basis, a fuzzy variable structure controller is designed to force the system state trajectory toward sliding surface and maintain on it then. The controller ensures the resulting closed-loop quadrotor system is asymptotically stable. Finally, a simulation is shown to verify the effectiveness of the proposed algorithm.

Temperature is a universe and significant data in production and scientific experiment, effective measurement and control of the temperature are widely applied in every part of national economy. Therefore, temperature control has great significance; relevant requirements regarding temperature control are also higher and higher. Temperature command system for animal house is a complex system with features of time varying, nonlinear, inaccurate model and unknown operating circumstance, temperature controlled by air-conditioner is less accurate and will affect the stability of the system performance. Regarding existing problems of long responding time, less stability and other disadvantages of the conventional PID control, we hereby raise a new approach of variable universe fuzzy PID control which is characterized with quick responding speed, better stability and stronger anti-interruption performance. Through the application of variable universe fuzzy control theory, which combined with the advantages of conventional PID and variable universe fuzzy control, forming the calculation of self-applied variable universe fuzzy control PID. Improve the accuracy of fuzzy inference by variable universe control theory, and realize the online tuning of PID data through fuzzy inference. Stimulate on Matlab, relevant stimulation results revealed that the control ways is clearly superior than conventional PID control, therefore effectively improve the dynamic property, stability precision and robustness, the system has better future of industrial application.

The sliding mode controller is used to speed up the error convergence when the error is greater than one. To reduce the error terminal sliding mode controller is recommended in this research. Fuzzy hype-plane variable sliding mode controller is adopted to guarantee the error convergence to zero in a finite time when the error is around the zero. The chattering in the conventional sliding model control systems is avoided with the employed continuous controller. To increase the system robustness in presence of uncertainty fuzzy logic controller is recommended. This technique is used to adjust the band of terminals. The simulation results show that the proposed scheme has strong robust against the uncertainties and disturbances, as well as leads to the convergence of the output to the desired value quickly and precisely than employing either sliding mode controller or terminal sliding mode controller alone.

During the process of aircraft landing, the control manners of carrier-based aircraft are always used to study. This is one of the important ways to avoid pilot-aircraft adverse coupling. In order to keep the accuracy of landing, this paper presents an improved asymmetric variable universe adaptive landing fuzzy controller with Safe Flight Area during approach for carrier-based aircraft. By means of universe-conversion factors and contraction–expansion factors, universe of discourse can be modified online, and fuzzy rules can reproduce automatically to adapt to the modified universe of discourse. The model simulation results indicate the better performance of the new method in comparison with the traditional controller with more accuracy and practicability.

According to this research paper, fuzzy hype plane controller is applied to robust nonlinear controller to reduce the vibration of motor. To control of multi degree of freedom joint, nonlinear controllers are the best candidate. Sliding mode controller is one of the best choices to robust control of this nonlinear system. The sliding mode controller is used to speed up the error convergence when the error is greater than one. To reduce the error terminal sliding mode controller is recommended in this research. Fuzzy hype-plane variable sliding mode controller is adopted to guarantee the error convergence to zero in a finite time when the error is near the zero. The chattering in the conventional sliding model control systems is avoided with the employed continuous controller. To increase the system robustness in presence of uncertainty fuzzy logic controller is recommended. This technique is used to adjust the band of terminals. The simulation results show that the proposed scheme has strong robust against the uncertainties and disturbances, as well as leads to the convergence of the output to the desired value quickly and precisely than employing either sliding mode controller or terminal sliding mode controller alone.

This paper proposed a novel fuzzy Variable Structure Controller (VSC) design for uncertain T-S fuzzy system, while neither assuming the control matrices of each local T-S model to be same nor knowing the priori information of the upper norm-bounds of uncertainties. The existence condition of desired sliding surfaces and the asymptotical stability of the reduced-order equivalent sliding-mode dynamics are firstly derived by solving a set of linear matrix inequalities (LMIs). After the establishment of the local T-S model, the upper norm-bounds of uncertainties and modeling errors between the original and local systems are estimated. Then, the designed VSC controller composes of a state-feedback control term and an adaptive switching-feedback control term, which are achieved based on the Lyapunov function method. Finally, a numerical example is illustrated in order to show the effectiveness of the proposed methods.

To confirm the fuzzy rules satisfying given approaching error of variable universe adaptive fuzzy controller, this paper designs a universal approaching error boundary of adaptive fuzzy controller. With explicating the structure of variable universe adaptive fuzzy controller, the approaching error boundary of Single-Input and Single-Output or Multi-Input and Multi-Output fuzzy control system should be ascertained respectively for physical condition. The model simulation results indicate the better performance of the new method in comparison with the traditional controller with more accuracy and practicability.

The paper firstly describes some typical manual and automatic semantic annotation, then analyses strategy of semantic annotation based on ontology. Secondly, this paper proposes semantic hierarchical structure of fuzzy ontology based on Variable Precision Rough Set and concept lattice in order to make up the shortage of traditional semantic annotation methods. Novel algorithm of semantic annotation based on fuzzy ontology for domain Webpage is presented in the paper by comparing ontology data of extractive webpage. Finally, experiments show that this presented algorithm is better than the traditional method in semantic annotation rate of support and recall.

To improve the accuracy of the state evaluation is the most important for the relay protection state maintenance. Therefore, a fuzzy synthetic evaluation of relay protection based on variable weight value is proposed in this paper. Pick up the major factor of the device and secondary circuit of relay protection to establish a reasonable index system of state evaluation. Then according to the variable weight comprehensive concept of factor space theory, we introduce the balance function to adjust the weight coefficient of each factor. Final, get the membership function in triangle trapezoid distribution to show the condition information, and select synthetic operator, then an evaluation system of relay protection with objectively and accurately is proposed. Running examples show that after dealing with the variable weight, some factors weight value become larger or smaller, so running state of evaluate factor may be changed, make it close to the real running state.

Ontology is an important tool on the semantic web, for ontology has a powerful expressive ability on knowledge representation. However, it has no ability to represent the imprecise information as well as the uncertainty. In order to share and deal with the fuzzy knowledge between homogeneous systems on the semantic web, in this paper, the concept of linguistic variable ontology is proposed as the basis of Fuzzy System. Taking a flu fuzzy diagnosis case for example, fuzzy system is modeled. For fuzzy knowledge representation and further inference, the fuzzy rule base is constructed, which constitutes a part of fuzzy system. A tool of protégé is used to realize the fuzzy system and a plugin of SWRLTab for the fuzzy rule. Also, SWRL/OWL is used to represent the rules formally for facilitating machine understanding and process. Comparisons are made between two fuzzy tools to illustrate the advantages of SWRLTab. The paper concludes that linguistic variable ontology-based diagnosis system can achieve the fuzzy knowledge sharing between homogeneous systems correctly.

Maintaining phosphoric slurry within the optimal temperature range is of great significance in improving the quality of the phosphoric acid product. Due to the lack of integral parts and the limited number of expert rules, general fuzzy controllers are commonly incapable of achieving accurate output. This paper proposed a novel fuzzy controller based on variable universes of discourse (VUD) to improve the accuracy of the temperature of phosphoric slurry. By tuning the motorized motor in the vacuum pump, the controller can obtain an appropriate vacuum degree in flash evaporators. With the online operation of contraction-expansion factors, fuzzy rules can be reproduced online, which is equivalent to the densification of fuzzy sets on universes of discourse. Thus, the output accuracy of VUD fuzzy controllers can be enhanced significantly. The experimental results show that VUD fuzzy controllers can tune the temperature of phosphoric slurry to within the optimal range and have a much better effect than general fuzzy controllers.

본 논문에서는 쓰레기 소각로의 효율적인 연소 제어를 위해 쓰레기 소각 플랜트의 다변수 퍼지 모델을 구한다. 먼저 복잡하고 비선형 시스템인 소각로의 모델을 구하기 위해 다변수 퍼지 모델링을 수행한다. 얻어진 다변수 퍼지 모델은 주어지는 입력에 대해 소각로의 출력을 정확하게 예측한다. 그리고 얻어진 퍼지 모델은 시뮬레이터 구현에 사용되어 소각로의 출력예측에 의한 제어전략의 구축 및 운전자의 훈련 등에 사용되는 운전보조 시뮬레이션 시스템을 구현할 수 있다.

In this paper, multi-variable fuzzy model for efficient combustion control of refuse incineration plant is obtained. First, to obtain model of incineration plant which is complex and nonlinear multi-variable fuzzy modeling is performed. Obtained multi-variable fuzzy model predicts outputs of incinerator almost exactly. Then using multi-variable fuzzy model we can build simulator which is used as operation simulator for building of control strategy and training of operator.

Recently, Xue et al. [Computers and Mathematics with Applications 55 (2008) 1215-1224] proposed a formula for the expected value of a function of a fuzzy variable based on the assumption that the fuzzy variable has a continuous membership function. In conclusion, they remained the case where the membership function of the fuzzy variable is discontinuous for the future research, and then expected to get similar results. Thus this note is to propose a new formula for the expected value of a function of a general fuzzy variable which is not restricted on having a continuous membership function. Furthermore, we give an example which cannot be applied to the formula that Xue et al. proposed. We also use the same example given by Xue et al. to show how to apply the new formula.

In this paper, for the fuzzy-reaching law method which has the characteristic of elimination of chattering at sliding mode as well as the characteristic of fast response at the design of variable structure controller with reaching law, optimal solutions for the determination of parameters of fuzzy membership functions by using genetic algorithm are proposed. Generally, the design of fuzzy controller has difficulties in determining the parameters of fuzzy membership functions by using a tedious trial-and-error process. To overcome these difficulties, this paper develops genetic algorithm of an optimal searching method based on genetic operation, and to verify the validity of this proposed method it is simulated through 2 link robot manipulator.

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

In this paper, the output-feedback control for stabilizing the uncertain nonlinear system is proposed. For achieving the robust stability, we deal with the parametric uncertainties of the concerned system which is based on the Takagi-Sugeno (T-S) fuzzy model. Also, we derive the observer-based models preserving the property and structure of the uncertainties. The sufficient conditions for output feedback stabilizing controller designs are given in terms of solutions to a set of linear matrix inequalities (LMIs). The simulation results for variable speed wind power (VSWP) system are demonstrated to visualize the feasibility of the proposed method.

This paper deals with fuzzy control with a feedforward compensator to progress both energy saving and coefficient of performance (COP) in a variable speed refrigeration system. Both the capacity and superheat are controlled simultaneously and independently in the system. By adopting the fuzzy theory, the controller design for the capacity and superheat is possible without depending on a dynamic model of the system. Moreover, the feedforward compensator of the superheat can reduce influence of the interfering loop between the capacity and superheat. Some experiments are conducted to design appropriate fuzzy controller by an iteration manner. The results show that the proposed fuzzy controller with the compensator can establish good control performances for the complicated refrigeration system in spite of its inherent strong non-linearity. Also, the fuzzy control performances were investigated by comparing to the model based PI control experimental results to evaluate transient behavior under the control.

In this paper, we suggest fuzzy control with feedforward compensator of superheat to progress both energy saving and coefficient of performance(COP) in a variable speed refrigeration system. The capacity and superheat are controlled simultaneously and independently by an inverter and an electronic expansion valve respectively for saving energy and improving COP in the system. By adopting the fuzzy control. the controller design for the capacity and superheat is possible without depending on a dynamic model of the system. Moreover, the feedforward compensator of the superheat can eliminate influence of the interfering loop between capacity and superheat. Some experiments are conducted to design the appropriate fuzzy controller by an iteration manner. The results show that the proposed fuzzy controller with the compensator can establish good control performances for the complicated refrigeration system with inherent strong non-linearity.

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.