Showing papers on "Fuzzy logic published in 2014"

Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets

Abstract: Recently, a new model based on Pythagorean fuzzy set PFS has been presented to manage the uncertainty in real-world decision-making problems. PFS has much stronger ability than intuitionistic fuzzy set to model such uncertainty. In this paper, we define some novel operational laws of PFSs and discuss their desirable properties. For the multicriteria decision-making problems with PFSs, we propose an extended technique for order preference by similarity to ideal solution method to deal effectively with them. In this approach, we first propose a score function based comparison method to identify the Pythagorean fuzzy positive ideal solution and the Pythagorean fuzzy negative ideal solution. Then, we define a distance measure to calculate the distances between each alternative and the Pythagorean fuzzy positive ideal solution as well as the Pythagorean fuzzy negative ideal solution, respectively. Afterward, a revised closeness is introduced to identify the optimal alternative. At length, a practical example is given to illustrate the developed method and to make a comparative analysis.

Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms

Abstract: Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do so, a Multi-Objective Evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.

Fuzzy Relation Equations and Their Applications to Knowledge Engineering

Abstract: 1: Introductory Remarks on Fuzzy Sets.- 2: Fuzzy Relation Equations in Residuated Lattices.- 3: Lower Solutions of Max-Min Fuzzy Equations.- 4: Measures of Fuzziness of Solutions of Max-Min Fuzzy Relation Equations on Linear Lattices.- 5: Boolean Solutions of Max-Min Fuzzy Equations.- 6: ?-Fuzzy Relation Equations and Decomposable Fuzzy Relations.- 7: Max-Min Decomposition Problem of a Fuzzy Relation in Linear Lattices.- 8: Fuzzy Relation Equations with Lower and Upper Semicontinuous Triangular Norms.- 9: Fuzzy Relation Equations with Equality and Difference Composition Operators.- 10: Approximate Solutions of Fuzzy Relation Equations.- 11: Handling Fuzziness in Knowledge-Based Systems.- 12: Construction of Knowledge Base, Its Validation and Optimization.- 13: Inference Algorithms in Knowledge-Based Systems.- 14: A Fuzzy Controller and Its Realization.- 15: Bibliographies.- Author Index.

Observer-Based Adaptive Decentralized Fuzzy Fault-Tolerant Control of Nonlinear Large-Scale Systems With Actuator Failures

Abstract: This paper investigates the adaptive fuzzy decentralized fault-tolerant control (FTC) problem for a class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear system contains the unknown nonlinear functions, i.e., unmeasured states and actuator faults, which are modeled as both loss of effectiveness and lock-in-place. With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is designed to estimate the unmeasured states. By combining the backstepping technique with the nonlinear FTC theory, a novel adaptive fuzzy decentralized FTC scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded, and the tracking errors between the system outputs and the reference signals converge to a small neighborhood of zero by appropriate choice of the design parameters. Simulation results are provided to show the effectiveness of the control approach.

Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies

Abstract: Construction projects are initiated in dynamic environment which result in circumstances of high uncertainty and risks due to accumulation of many interrelated parameters. The purpose of this study is to use novel analytic tools to evaluate the construction projects and their overall risks under incomplete and uncertain situations. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies and counteract the high-risk factors. The study covers identifying the key risk criteria of construction projects at King Abdulaziz University (KAU), and assessing the criteria by the integrated hybrid methodologies. The proposed hybrid methodologies were initiated with a survey for data collection. The relative importance index (RII) method was applied to prioritize the project risks based on the data obtained. The construction projects were then categorized by fuzzy AHP and fuzzy TOPSIS methodologies. Fuzzy AHP (FAHP) was used to create favorable weights for fuzzy linguistic variable of construction projects overall risk. The fuzzy TOPSIS method is very suitable for solving group decision making problems under the fuzzy environment. It attempted to incorporate vital qualitative attributes in performance analysis of construction projects and transformed the qualitative data into equivalent quantitative measures. Thirty construction projects were studied with respect to five main criteria that are the time, cost, quality, safety and environment sustainability. The results showed that these novel methodologies are able to assess the overall risks of construction projects, select the project that has the lowest risk with the contribution of relative importance index. This approach will have potential applications in the future.

Fuzzy Mathematical Programming: Methods and Applications

Abstract: 1 Introduction.- 1.1 Objectives of This Study.- 1.2 Fuzzy Mathematical Programming Problems.- 1.3 Classification of Fuzzy Mathematical Programming.- 1.4 Applications of Fuzzy Mathematical Programming.- 1.5 Literature Survey.- 2 Fuzzy Set Theory.- 2.1 Fuzzy Sets.- 2.2 Fuzzy Set Theory.- 2.2.1 Basic Terminology and Definition.- 2.2.1.1 Definition of Fuzzy Sets.- 2.2.1.2 Support.- 2.2.1.3 ?-level Set.- 2.2.1.4 Normality.- 2.2.1.5 Convexity and Concavity.- 2.2.1.6 Extension Principle.- 2.2.1.7 Compatibility of Extension Principle with ?-cuts.- 2.2.1.8 Relation.- 2.2.1.9 Decomposability.- 2.2.1.10 Decomposition Theorem.- 2.2.1.11 Probability of Fuzzy Events.- 2.2.1.12 Conditional Fuzzy Sets.- 2.2.2 Basic Operations.- 2.2.2.1 Inclusion.- 2.2.2.2 Equality.- 2.2.2.3 Complementation.- 2.2.2.4 Intersection.- 2.2.2.5 Union.- 2.2.2.6 Algebraic Product.- 2.2.2.7 Algebraic Sum.- 2.2.2.8 Difference.- 2.3 Membership Functions.- 2.3.1 A Survey of Functional Forms.- 2.3.2 Examples to Generate Membership Functions.- 2.3.2.1 Distance Approach.- 2.3.2.2 True-Valued Approach.- 2.3.2.3 Payoff Function.- 2.3.2.4 Other Examples.- 2.4 Fuzzy Decision and Operators.- 2.4.1 Fuzzy Decision.- 2.4.2 Max-Min Operator.- 2.4.3 Compensatory Operators.- 2.4.3.1 Numerical Example for Operators.- 2.5 Fuzzy Arithmetic.- 2.5.1 Addition of Fuzzy Numbers.- 2.5.2 Subtraction of Fuzzy Numbers.- 2.5.3 Multiplication of Fuzzy Numbers.- 2.5.4 Division of Fuzzy Numbers.- 2.5.5 Triangular and Trapezoid Fuzzy Numbers.- 2.6 Fuzzy Ranking.- 3 Fuzzy Mathematical Programming.- 3.1 Fuzzy Linear Programming Models.- 3.1.1 Linear Programming Problem with Fuzzy Resources.- 3.1.1.1 Verdegay's Approach.- 3.1.1.1a Example 1: The Knox Production-Mix Selection Problem.- 3.1.1.1b Example 2: A Transportation Problem.- 3.1.1.2 Werners's Approach.- 3.1.1.2a Example 1: The Knox Production-Mix Selection Problem.- 3.1.1.2b Example 2: An Air Pollution Regulation Problem.- 3.1.2 Linear Programming Problem with Fuzzy Resources and Objective.- 3.1.2.1 Zimmermann's Approach.- 3.1.2.1a Example 1: The Knox Production-Mix Selection Problem.- 3.1.2.1b Example 2: A Regional Resource Allocation Problem.- 3.1.2.1c Example 3: A Fuzzy Resource Allocation Problem.- 3.1.2.2 Chanas's Approach.- 3.1.2.2a Example 1: An Optimal System Design Problem.- 3.1.2.2b Example 2: An Aggregate Production Planning Problem.- 3.1.3 Linear Programming Problem with Fuzzy Parameters in the Objective Function.- 3.1.4 Linear Programming with All Fuzzy Coefficients.- 3.1.4.1 Example: A Production Scheduling Problem.- 3.2 Interactive Fuzzy Linear Programming.- 3.2.1 Introduction.- 3.2.2 Discussion of Zimmermann's, Werners's Chanas's and Verdegay's Approaches.- 3.2.3 Interactive Fuzzy Linear Programming - I.- 3.2.3.1 Problem Setting.- 3.2.3.2 The Algorithm of IFLP-I.- 3.2.3.3 Example: The Knox Production-Mix Selection Problem.- 3.2.4 Interactive Fuzzy Linear Programming - II.- 3.2.4.1 The Algorithm of IFLP-II.- 3.3 Some Extensions of Fuzzy Linear Programming Problems.- 3.3.1 Membership Functions.- 3.3.1.1 Example: A Truck Fleet Problem.- 3.3.2 Operators.- 3.3.3 Sensitivity Analysis and Dual Theory.- 3.3.4 Fuzzy Non-Linear Programming.- 3.3.4.1 Example: A Fuzzy Machining Economics Problem.- 3.3.5 Fuzzy Integer Programming.- 3.3.5.1 Fuzzy 0-1 Linear Programming.- 3.3.5.1a Example: A Fuzzy Location Problem.- 4 Possibilistic Programming.- 4.1 Possibilistic Linear Programming Models.- 4.1.1 Linear Programming with Imprecise Resources and Technological Coefficients.- 4.1.1.1 Ramik and Rimanek's Approach.- 4.1.1.1a Example: A Profit Apportionment Problem.- 4.1.1.2 Tanaka, Ichihashi and Asai's Approach.- 4.1.1.3 Dubois's Approach.- 4.1.2 Linear Programming with Imprecise Objective Coefficients.- 4.1.2.1 Lai and Hwang's Approach.- 4.1.2.1a Example: A Winston-Salem Development Management Problem.- 4.1.2.2 Rommelfanger, Hanuscheck and Wolf's Approach.- 4.1.2.3 Delgado, Verdegay and Vila's Approach.- 4.1.3 Linear Programming with Imprecise Objective and Technological Coefficients.- 4.1.4 Linear Programming with Imprecise Coefficients.- 4.1.4.1 Lai and Hwang's Approach.- 4.1.4.2 Buckley's Approach.- 4.1.4.2a Example: A Feed Mix (Diet) Problem.- 4.1.4.3 Negi's Approach.- 4.1.4.4 Fuller's Approach.- 4.1.5 Other Problems.- 4.2 Some Extensions of Possibilistic Linear Programming.- 4.2.1 Linear Programming with Imprecise Coefficients and Fuzzy Inequalities.- 4.2.1a Example: A Fuzzy Matrix Game Problem.- 4.2.2 Linear Programming with Imprecise Objective Coefficients and Fuzzy Resources.- 4.2.2a Example: A Bank Hedging Decision Problem.- 4.2.3 Stochastic Possibilistic Linear Programming.- 4.2.3a Example: A Bank Hedging Decision Problem.- 5 Concluding Remarks.- 5.1 Probability Theory versus Fuzzy Set Theory.- 5.2 Stochastic versus Possibilistic Programming.- 5.3 Future Research.- 5.4 Introduction of the Following Volume.- 5.5 Fuzzy Multiple Attribute Decision Making.- Books, Monographs and Conference Proceedings.- Journal Articles, Technical Reports and Theses.

A Comparative Study of Energy Management Schemes for a Fuel-Cell Hybrid Emergency Power System of More-Electric Aircraft

Abstract: This paper presents a comparative analysis of different energy management schemes for a fuel-cell-based emergency power system of a more-electric aircraft. The fuel-cell hybrid system considered in this paper consists of fuel cells, lithium-ion batteries, and supercapacitors, along with associated dc/dc and dc/ac converters. The energy management schemes addressed are state of the art and are most commonly used energy management techniques in fuel-cell vehicle applications, and they include the following: the state machine control strategy, the rule-based fuzzy logic strategy, the classical proportional-integral control strategy, the frequency decoupling/fuzzy logic control strategy, and the equivalent consumption minimization strategy. The main criteria for performance comparison are the hydrogen consumption, the state of charges of the batteries/supercapacitors, and the overall system efficiency. Moreover, the stresses on each energy source, which impact their life cycle, are measured using a new approach based on the wavelet transform of their instantaneous power. A simulation model and an experimental test bench are developed to validate all analysis and performances.

Fuzzy Sampled-Data Control for Uncertain Vehicle Suspension Systems

Abstract: This paper investigates the problem of sampled-data $H_{\infty}$ control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy $H_{\infty}$ disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method.

Fuzzy analytic hierarchy process with interval type-2 fuzzy sets

Abstract: The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1's, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

Abstract: This paper focuses on designing interval type-2 (IT2) control for nonlinear systems subject to parameter uncertainties. To facilitate the stability analysis and control synthesis, an IT2 Takagi-Sugeno (T-S) fuzzy model is employed to represent the dynamics of nonlinear systems of which the parameter uncertainties are captured by IT2 membership functions characterized by the lower and upper membership functions. A novel IT2 fuzzy controller is proposed to perform the control process, where the membership functions and number of rules can be freely chosen and different from those of the IT2 T-S fuzzy model. Consequently, the IT2 fuzzy-model-based (FMB) control system is with imperfectly matched membership functions, which hinders the stability analysis. To relax the stability analysis for this class of IT2 FMB control systems, the information of footprint of uncertainties and the lower and upper membership functions are taken into account for the stability analysis. Based on the Lyapunov stability theory, some stability conditions in terms of linear matrix inequalities are obtained to determine the system stability and achieve the control design. Finally, simulation and experimental examples are provided to demonstrate the effectiveness and the merit of the proposed approach.

Fuzzy Adaptive Actuator Failure Compensation Control of Uncertain Stochastic Nonlinear Systems With Unmodeled Dynamics

Abstract: This paper investigates fuzzy adaptive actuator failure compensation control for a class of uncertain stochastic nonlinear systems in strict-feedback form. These stochastic nonlinear systems contain the actuator faults of both loss of effectiveness and lock-in-place, unmodeled dynamics, and without direct measurements of state variables. With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is established to estimate the unmeasured states. By introducing the dynamical signal and the changing supply function technique design into the backstepping control design, a robust adaptive fuzzy fault-tolerant control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are bounded in probability in the presence of the actuator failures and the unmodeled dynamics. Simulation results are provided to show the effectiveness of the control approach.

Cooperative Adaptive Fuzzy Tracking Control for Networked Unknown Nonlinear Multiagent Systems With Time-Varying Actuator Faults

Abstract: In this paper, the cooperative adaptive fault tolerant fuzzy tracking control (CAFTFTC) problem of networked high-order multiagent with time-varying actuator faults is studied, and a novel CAFTFTC scheme is proposed to guarantee that all follower nodes asymptotically synchronize a leader node with tracking errors converging to a small adjustable neighborhood of the origin in spite of actuator faults. The leader node is modeled as a higher order nonautonomous nonlinear system. It acts as a command generator giving commands only to a small portion of the networked group. Each follower is assumed to have nonidentical unknown nonlinear dynamics, and the communication network is also assumed to be a weighted directed graph with a fixed topology. A distributed robust adaptive fuzzy controller is designed for each follower node such that the tracking errors are cooperative uniform ultimate boundedness (CUUB). Moreover, these controllers are distributed in the sense that the controller designed for each follower node only requires relative state information between itself and its neighbors. The adaptive compensation term of the optimal approximation errors and external disturbances is adopted to reduce the effects of the errors and disturbances, which removes the assumption that the upper bounds of unknown function approximation errors and disturbances should be known. Analysis of stability and parameter convergence of the proposed algorithm are conducted that are based on algebraic graph theory and Lyapunov theory. Comparing with results in the literature, the CAFTFTC scheme can minimize the time delay between fault occurrence and accommodation and reduce its adverse effect on system performance. In addition, the FTC scheme requires no additional fault isolation model, which is necessary in the traditional active FTC scheme. Finally, an example is provided to validate the theoretical results.

Reliable Filtering With Strict Dissipativity for T-S Fuzzy Time-Delay Systems

Abstract: In this paper, the problem of reliable filter design with strict dissipativity has been investigated for a class of discrete-time T-S fuzzy time-delay systems. Our attention is focused on the design of a reliable filter to ensure a strictly dissipative performance for the filtering error system. Based on the reciprocally convex approach, firstly, a sufficient condition of reliable dissipativity analysis is proposed for T-S fuzzy systems with time-varying delays and sensor failures. Then, a reliable filter with strict dissipativity is designed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, numerical examples are provided to illustrate the effectiveness of the developed techniques.

Operators and Comparisons of Hesitant Fuzzy Linguistic Term Sets

Abstract: The theory of hesitant fuzzy linguistic term sets (HFLTSs) is very useful in objectively dealing with situations in which people are hesitant in providing linguistic assessments. The purpose of this paper is to develop comparison methods and study the aggregation theory for HFLTSs. We first define operations on HFLTSs and give possibility degree formulas for comparing HFLTSs. We then define two aggregation operators for HFLTSs: a hesitant fuzzy LWA operator and a hesitant fuzzy LOWA operator. In actual application, we use these operators and the comparison methods to deal with multicriteria decision-making problems with different situations in which importance weights of criteria or experts are known or unknown.

Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms

Abstract: Evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general. However, when problems with many objectives are encountered, nearly all algorithms perform poorly due to loss of selection pressure in fitness evaluation solely based upon the Pareto optimality principle. In this paper, we introduce a new fitness evaluation mechanism to continuously differentiate individuals into different degrees of optimality beyond the classification of the original Pareto dominance. The concept of fuzzy logic is adopted to define a fuzzy Pareto domination relation. As a case study, the fuzzy concept is incorporated into the designs of NSGA-II and SPEA2. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the original ones for solving many-objective optimization problems.

Real-Time Energy Management Algorithm for Plug-In Hybrid Electric Vehicle Charging Parks Involving Sustainable Energy

Abstract: In this paper, a real-time energy management algorithm (RTEMA) for a grid-connected charging park in an industrial/commercial workplace is developed. The charging park under study involves plug-in hybrid electric vehicles (PHEVs) with different sizes and battery ratings as well as a photovoltaic (PV) system. Statistical and forecasting models were developed as components in the developed RTEMA to model the various uncertainties involved such as the PV power, the PHEVs, arrival time, and the energy available in their batteries upon their arrival. The developed energy management algorithm aims at reducing the overall daily cost of charging the PHEVs, mitigating the impact of the charging park on the main grid, and contributing to shaving the peak of the load curve. Hence, the benefits of implementing this RTEMA is shared among the customers, the charging park considering all customers as a bulk of power connected to the grid, and the ac grid. This makes it applicable for various business models. The developed RTEMA utilizes a fuzzy controller to manage the random energy available in the PHEVs' batteries arriving at the charging park and their charging/discharging times, power sharing among individual PHEVs that is commonly known as vehicle-to-vehicle functionality, and vehicle-to-grid service between the charging park and the main ac grid. The developed RTEMA was simulated using the standard IEEE 69-bus system at different penetration and distribution levels. The obtained results verify the effectiveness and validity of the developed RTEMA.

Fuzzy-Logic-Controller-Based SEPIC Converter for Maximum Power Point Tracking

Abstract: This paper presents a fuzzy logic controller (FLC)-based single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) operation of a photovoltaic (PV) system. The FLC proposed presents that the convergent distribution of the membership function offers faster response than the symmetrically distributed membership functions. The fuzzy controller for the SEPIC MPPT scheme shows high precision in current transition and keeps the voltage without any changes, in the variable-load case, represented in small steady-state error and small overshoot. The proposed scheme ensures optimal use of PV array and proves its efficacy in variable load conditions, unity, and lagging power factor at the inverter output (load) side. The real-time implementation of the MPPT SEPIC converter is done by a digital signal processor (DSP), i.e., TMS320F28335. The performance of the converter is tested in both simulation and experiment at different operating conditions. The performance of the proposed FLC-based MPPT operation of SEPIC converter is compared to that of the conventional proportional-integral (PI)-based SEPIC converter. The results show that the proposed FLC-based MPPT scheme for SEPIC can accurately track the reference signal and transfer power around 4.8% more than the conventional PI-based system.

PANFIS: A Novel Incremental Learning Machine

Abstract: Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating human's interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.

Load Frequency Control of a Multi-Area Power System: An Adaptive Fuzzy Logic Approach

Abstract: In this paper, a new load frequency control (LFC) for multi-area power systems is developed based on the direct–indirect adaptive fuzzy control technique. LFCs for each area are designed based on availability of frequency deviation of each area and tie-line power deviation between areas. The fuzzy logic system approximation capabilities are exploited to develop suitable adaptive control law and parameter update algorithms for unknown interconnected LFC areas. An ${H}_{\infty}$ tracking performance criterion is introduced to minimize the approximation errors and the external disturbance effects. The proposed controller guarantees stability of the overall closed-loop system. Simulation results for a real three-area power system prove the effectiveness of the proposed LFC and show its superiority over a classical PID controller and a type-2 fuzzy controller.

Sampled-Data Fuzzy Control of Chaotic Systems Based on a T–S Fuzzy Model

Abstract: In this paper, a sampled-data fuzzy controller is designed to stabilize a class of chaotic systems. A Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic systems. Based on this general model, the exponential stability issue of the closed-loop systems with an input constraint is first investigated by a novel time-dependent Lyapunov functional, which is positive definite at sampling times but not necessary between the sampling times. Then, two sufficient conditions are developed for sampled-data fuzzy controller synthesis of the underlying T-S fuzzy model with or without input constraint. All the proposed results in this paper depend on both the upper and lower bounds on a sampling interval, and the available information about the actual sampling pattern is fully utilized. The proposed sampled-data fuzzy control scheme is successfully applied to the chaotic Lorenz system, which is shown to be effective and less conservative compared with existing results.

Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dc-dc converter

Abstract: This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc-dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc-dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.

Adaptive Fuzzy Robust Output Feedback Control of Nonlinear Systems With Unknown Dead Zones Based on a Small-Gain Approach

Abstract: In this paper, an adaptive fuzzy robust output feedback control problem is considered for a class of single-input and single-output nonlinear systems in a strict-feedback form. The considered systems possess the unstructured uncertainties, unknown dead zone, and the dynamics uncertainties, and they do not assume the states being available for the controller design. In the controller design, fuzzy logic systems are first used to approximate the unstructured uncertainties, and by utilizing the information of the bounds of the dead-zone slopes and treating the time-varying inputs coefficients as a system uncertainty, a fuzzy state observer is designed to estimate the unmeasured states. By combining a backstepping technique with a nonlinear small-gain approach, a new adaptive fuzzy robust output feedback control has been developed. It is proved that the proposed fuzzy adaptive control approach can guarantee the semiglobal uniform ultimate boundedness for all the solutions of the closed-loop systems. Simulation studies and comparisons with previous methods are included to illustrate the effectiveness of the proposed approach.

General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial

Abstract: The purpose of this tutorial paper is to make general type-2 fuzzy logic systems (GT2 FLSs) more accessible to fuzzy logic researchers and practitioners, and to expedite their research, designs, and use. To accomplish this, the paper 1) explains four different mathematical representations for general type-2 fuzzy sets (GT2 FSs); 2) demonstrates that for the optimal design of a GT2 FLS, one should use the vertical-slice representation of its GT2 FSs because it is the only one of the four mathematical representations that is parsimonious; 3) shows how to obtain set theoretic and other operations for GT2 FSs using type-1 (T1) FS mathematics (α- cuts play a central role); 4) reviews Mamdani and TSK interval type-2 (IT2) FLSs so that their mathematical operations can be easily used in a GT2 FLS; 5) provides all of the formulas that describe both Mamdani and TSK GT2 FLSs; 6) explains why center-of sets type-reduction should be favored for a GT2 FLS over centroid type-reduction; 7) provides three simplified GT2 FLSs (two are for Mamdani GT2 FLSs and one is for a TSK GT2 FLS), all of which bypass type reduction and are generalizations from their IT2 FLS counterparts to GT2 FLSs; 8) explains why gradient-based optimization should not be used to optimally design a GT2 FLS; 9) explains how derivative-free optimization algorithms can be used to optimally design a GT2 FLS; and 10) provides a three-step approach for optimally designing FLSs in a progressive manner, from T1 to IT2 to GT2, each of which uses a quantum particle swarm optimization algorithm, by virtue of which the performance for the IT2 FLS cannot be worse than that of the T1 FLS, and the performance for the GT2 FLS cannot be worse than that of the IT2 FLS.

Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

Abstract: This paper presents an edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection For the defuzzification process, the heights and approximation methods are used Simulation results with a type-1 fuzzy inference system, an interval type-2 fuzzy inference system, and with a generalized type-2 fuzzy inference system for edge detection are presented The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic

A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection

Abstract: City logistics (CL) tends to increase efficiency and mitigate the negative effects of logistics processes and activities and at the same time to support the sustainable development of urban areas. Accordingly, various measures and initiatives are applying and various conceptual solutions are defining. The effects vary depending on the characteristics of the city. This paper proposes a framework for the selection of the CL concept which would be most appropriate for different participants, stakeholders, and which would comply with attributes of the surroundings. CL participants have different, usually conflicting goals and interests, so it is necessary to define a large number of criteria for concepts evaluation. On the other hand, the importance of the criteria is dependent on the specific situation, i.e., a large number of factors describing the surroundings. In situations like this, selecting the best alternative is a complex multi-criteria decision-making (MCDM) problem consisting of conflicting and uncertain elements. A novel hybrid MCDM model that combines fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), fuzzy Analytical Network Process (ANP) and fuzzy Visekriterijumska Optimizacija i kompromisno Resenje (VIKOR) methods is developed in this paper. The model provides support to decision makers (planners, city administration, logistics service providers, users, etc.) when selecting the CL concept, which is successfully performed in this paper for the City of Belgrade.