Fuzzy Modeling Technique Research Articles

Active Fault-Tolerant and Attack-Resilient Control for a Renewable Microgrid Against Power-Loss Faults and Data Integrity Attacks.

The next-generation power grid evolves from the development of fundamental cyber-physical energy systems called smart microgrids. In order to improve the reliability, safety, and security of smart microgrids and achieve a more cost-effective operation, innovative approaches for physical fault diagnosis and fault-tolerant control (FTC) as well as intrusion detection and attack-resilient control (ARC) should be investigated. Given that, this article considers a smart hybrid renewable-based microgrid with different types of distributed generation units, including solar photovoltaic (PV) array, wind turbines, and battery energy storage system. Novel active FTC and ARC strategies are designed for pulse-width modulation (PWM) converters at microgrid level. The proposed fault-tolerant controller is based on an optimal fuzzy gain-scheduling technique that is used to accommodate the adverse impacts of PV power-loss faults. Also, the proposed attack-resilient controller relies on the estimated values of sensor measurements during the occurrence of data integrity cyber-attacks. To access and evaluate the microgrid's real-time health status, both FTC and ARC strategies employ an integrated model-based intrusion detection and fault diagnosis (IDFD) system that is designed using a fuzzy modeling and identification technique. Finally, the effectiveness of the proposed solutions is demonstrated via a series of simulations in MATLAB/Simulink using an advanced microgrid benchmark.

Interval Type-2 Fuzzy-Model-Based Sampled-Data Control of an AUV Depth System with Input Saturation

This paper proposes a sampled-data fuzzy controller design technique for an autonomous underwater vehicle (AUV) depth system represented by an interval type-2 (IT-2) fuzzy model, considering input saturation. In the Takagi–Sugeno (T–S) fuzzy model of an AUV depth system, surge velocity is chosen as a premise variable. To address the associated uncertainty with this variable, we employ the IT-2 fuzzy modeling technique. Also, the controller proposed in this paper utilizes time-varying gains, ensuring superior exponential stability compared with traditional fixed gain approaches. Furthermore, a membership function-dependent (MFD) H∞ criterion is employed to enhance robustness for each subsystem individually. Taking into account the mentioned aspects, the controller design condition is derived in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is validated through simulation examples.

Active Adaptive Observer-Based Fault-Tolerant Control Strategy for a Class of T–S Fuzzy Systems With Unmeasurable Premise Variables

This paper addresses the problem of fault-tolerant output tracking control for a class of Takagi-Sugeno (T-S) fuzzy systems with unmeasurable premise variables subject to additive and multiplicative actuator faults and external disturbances. In nominal conditions, utilizing a quadratic Lyapunov function and non-parallel distributed compensation (non-PDC) technique, the suggested strategy delivers linear matrix inequality (LMI)-based constraints. Simultaneously design of the proportional-integral (PI)-like state feedback controller and fuzzy anti-windup compensator is achieved with the aim of output tracking. In the faulty case, by considering the nominal system as a reference model, a direct adaptive projection-based approach is developed using the T-S fuzzy modeling and control techniques to supply the adaptive fault-tolerant controller components. An enhanced proportional-integral (PI) state/fault observer with unmeasurable premise variables is introduced only to provide the estimation of states to be used in the proposed controller. The overall closed-loop system ensures the uniformly ultimately bounded (u.u.b) solutions for error dynamics. Two examples, subsuming an inverted pendulum and a chaotic power system, have been used to present the merits and efficiency of the suggested approach persuasively.

Stabilization analysis of fractional-order nonlinear permanent magnet synchronous motor model via interval type-2 fuzzy memory-based fault-tolerant control scheme

The main objective of this study is to improve the convergence rate performance and analyze the stability properties of the FOPMSM model considering the load-torque external disturbances and actuator faults. Due to the complex nonlinearity, the presented FONPMSM model in the d−q frame is approximated by an IT-2 T–S fuzzy modeling technique. Besides, the fuzzy memory-based FTC is designed to eliminate the typical characteristics of chaotic behaviors and stabilize the proposed nonlinear model even if load torque disturbances, actuator faults in the controller, and time delays occur. Further, by employing the fractional order-based fuzzy LKF, some sufficient conditions are carried out in terms of LMIs to guarantee the asymptotic stability conditions, and simultaneously, disturbance reduction is confirmed. And then, the desired control gain matrices are determined from solvable LMIs, which can help to enhance the system stability performance. Finally, the numerical simulation of T–S fuzzy-based FOPMSM model is given to validate the applicability and efficiency of the proposed controller.

A novel hybrid approach GREG-fuzzy-GA for minimizing work piece temperature during 2.5D milling of Inconel625 super alloy

PurposeMilling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.Design/methodology/approachResponse surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.FindingsSR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.Originality/valueThe GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

Adaptive Event-Triggered Control of Stochastic Nonlinear Systems With Unknown Dead Zone

We consider the tracking control problem for a class of stochastic nonlinear systems in strict-feedback structure via output feedback signal in this article. The controlled plant is assumed subject to unknown dead-zone input. By utilizing the fact that the unknown dead-zone input function can be modeled as a time-varying nonlinear function and a bounded disturbance, and selecting appropriate design parameters, we show that the effect of unknown dead zone can be compensated. We design a fuzzy state observer via fuzzy logic modeling technique to estimate the unknown system states. Then, we prove, via Lyapunov stability analysis, that the controlled plant is bounded in probability. In addition, all the signals in the closed-loop system are guaranteed to be globally bounded in probability. The tracking errors are also ensured to converge to a small neighborhood of the origin. Finally, to show the effectiveness of the proposed control strategy, a simulation example of one-link manipulator is presented in the simulation section.

Fuzzy modelling techniques for improving multi-label classification of software bugs

Fuzzy modelling techniques for improving multi-label classification of software bugs

Fuzzy modelling techniques for improving multi-label classification of software bugs

Fuzzy modelling techniques for improving multi-label classification of software bugs

Comparing socioeconomic vulnerability index and land cover indices: Application of fuzzy TOPSIS model and geographic information system

Vulnerability Assessment (VA) has numerous management implications, particularly for locating the most vulnerable areas affected by land cover degradation. This study aims to assess and compare the relationship between socioeconomic vulnerability (SEV) and land cover indices (LCIs) in summer rangelands (Natanz county) and winter rangelands (Aran-V-Bidgol county) in Isfahan Province, Iran. To assess the socioeconomic vulnerability index (SEVI), a survey was conducted. Based on the SEVI, summer and winter rangelands were classified and mapped using a combination of fuzzy TOPSIS model and geographic information system (GIS) techniques. Field research was conducted to estimate LCIs, including plant cover, plant yield, litter (dead biomass), sand and gravel, bare soil, and forage production. Finally, the correlations between SEVI and LCIs were verified, and the SEV of winter rangelands and summer rangelands was examined. Results showed the rangelands of Chah Robat, Bidhend, and Tar were the most vulnerable summer rangelands, while the rangelands of Chah Zard, Cheshmeh Sefid, and Kandeh Matin were the most vulnerable winter rangelands. Findings further revealed more LCIs, including total ground cover, the yield of shrubs, and total yield had a significant relationship with SEVI in summer rangelands compared to winter rangelands (yield of annual forbs and total yield). Moreover, there was a significant difference between SEVI, determined by the fuzzy TOPSIS model, in summer and winter rangelands. It can be concluded that while the fuzzy TOPSIS model and GIS can be used to gain a better understanding of VA, the type of rangeland has an impact on how well these techniques can assess the SEV of rangelands.

Investigation of addition of calcium phosphate ceramic to multilayer scaffold for bone applications with improved mechanical properties: Fuzzy logic analysis

In this study, combining an excellent intrinsic property of polylactic acid (PLA) with the unique properties of three-dimensional (3D) printing technique and coating of chitosan-hydroxyapatite (CHI-HA) is used with electrospun nanofibers for the regeneration of hard tissues. This study aims to fabricate a microstructural scaffold with a PLA base by 3D fused deposition modeling (FDM) technique. High surface-to-volume ratio, high porosity, flexibility in surface performance, and exceptional mechanical performance are just a few of the characteristics that the small-diameter fibers display. Utilizing an examination from a scanning electron microscope (SEM), the morphological research is carried out. Besides, the biological reaction of the scaffolds is studied in phosphate buffer saline (PBS) and simulated body fluid (SBF). The samples are examined for wet and dry biological behavior, by SEM. Moreover, mechanical analyzes, including compressive strength and porosity, are performed on the samples and the results are evaluated in existing number models. Besides, the fuzzy modeling technique is used to forecast the properties of samples before fabricating and examining them. The results generally show that the presence of HA nanoparticles improves mechanical and biological properties. Specifically, the obtained results show that the sample with 10 wt% of HA is capable of suitable mechanical, chemical, and biological properties compared to other samples.

Quantitative Assessment of Dynamic Stability Characteristics for Jet Transport in Sudden Plunging Motion

In this paper, we present a monitoring program of loss control prevention for airlines to enhance aviation safety and operational efficiency. The assessments of dynamic stability characteristics based on the approaches of oscillatory motion and eigenvalue motion modes for jet transport aircraft response to sudden plunging motions are demonstrated. A twin-jet transport aircraft encountering severe clear-air turbulence in transonic flight during the descending phase was examined as the study case. The flight results in sudden plunging motions with abrupt changes in attitude and gravitational acceleration (i.e., the normal load factor) are provided. Development of the required thrust and aerodynamic models with the flight data mining and the fuzzy logic modeling techniques was carried out. The oscillatory derivatives extracted from these aerodynamic models were then used in the study of variations in stability characteristics during the sudden plunging motion. The fuzzy logic aerodynamic models were utilized to estimate the nonlinear unsteady aerodynamics while performing numerical integration of flight dynamic equations. The eigenvalues of all motion modes were obtained during time integration. The positive real part of the eigenvalues is to indicate unstable motion. The dynamic stability characteristics during sudden plunging motion are easily judged by the values in positive or negative. The present quantitative assessment method is an innovation to examine possible mitigation concepts of accident prevention and promote the understanding of aerodynamic responses of the jet transport aircraft.

Evaluating energy financing considerations and sustainable energy innovation with the role of financial development and energy development.

The purpose of the study is to test the role of energy development in energy financing considerations for sustainable energy innovation and financial development. To achieve the study objective, a fuzzy decision-making modeling technique is applied. The results revealed that bank loans are now the main source of financing for innovation and creativity in Chinese business entities. Project-based financing might be replaced with collaborative and sustainable energy innovation (CSI), warranting energy development. Moreover, green financing loan schemes invest both public and private funds in sustainable energy innovation to capitalize on financial development through sustainable energy innovation. The consideration and application of financial consideration for sustainable energy innovation-financing projects or companies are limitless. Providing for screening energy development cooperation proposals with small financial payback hurdle rates might have large opportunity costs. There may be a case for governments to increase industrial growth, improve resource efficiency, and increase factor productivity while tackling climate change. Economic growth in China may have an even greater influence on environmental sustainability than in other countries. On such points, there is a need to pay the attention. If the suggested policy suggestions are implemented successfully, they would help to enhance the scope of financing considerations for sustainable energy innovation to uplift financial development through energy development mechanisms at the corporate level.

A novel multiple kernel fuzzy topic modeling technique for biomedical data

BackgroundText mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format.MethodsIn this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights.ResultsExtensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models.ConclusionWe have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.

Fuzzy Luenberger Observer Design for Nonlinear Flexible Joint Robot Manipulator

The process of controlling a Flexible Joint Robot Manipulator (FJRM) requires additional sensors for measuring the state variables of flexible joints. Therefore, taking the elasticity into account adds a lot of complexity as all the additional sensors must be taken into account during the control process. This paper proposes a nonlinear observer that controls FJRM, without requiring equipment sensors for measuring the states. The nonlinear state equations are derived in detail for the FJRM where nonlinearity, of order three, is considered. The Takagi–Sugeno Fuzzy Model (T-SFM) technique is applied to linearize the FJRM system. The Luenberger observer is designed to estimate the unmeasured states using error correction. The developed Luenberger observer showed its ability to control the FJRM by utilizing only the measured signal of the velocity of the motor. Stability analysis is implemented to improve the ability of the designed observer to stabilize the FJRM system. The developed observer is tested by simulation to evaluate the ability of the observer to estimate the unknown states. The results showed that the proposed control algorithm estimated the motor angle, gear angle, link angle, angular velocity of gear, and angular velocity of link with zero steady errors.

Irregular deviation of flight control surface monitoring for jet transport aircraft

PurposeThe purpose of this paper is to present the irregular deviation examination of flight control surfaces and the potential problem diagnosis of irregular deviations for the jet transport aircraft. A four-jet transport aircraft at transonic flight in cruise phase is the study case of the present article.Design/methodology/approachThe standard lift-to-drag ratio (L/D) and flight dynamic models are established through flight data mining and the fuzzy logic modeling technique based on the flight data of quick access recorder available in the Flight Operations Quality Assurance (FOQA) program of the airlines. The irregular deviations of flight control surfaces are examined by the standard L/D model-predicted results through sensitivity analysis. The contribution values in L/D deficiency are predicted by the deviations and the L/D derivatives of all influencing variables in Taylor series expansion. The potential problems due to irregular deviations can be excavated by the flight dynamic models through the analysis of in-flight stability and controllability.FindingsThe magnitude of stabilizer angle to the deficiency of L/D is the largest among the four control surfaces and elevator is the second one through the judgment of contribution values in L/D deficiency. The stabilizer has irregular deviations with obvious endplay problems of jackscrew, as found in the present study. The stabilizer is suggested to have the unscheduled maintenance for the flight control rigging.Research limitations/implicationsThe specific transport aircraft of the standard L/D model should be the best one in L/D performance among all transport aircraft in the fleet of the airlines. The present method is a new concept to monitor the irregular deviation of flight control surface. The study case of the four-jet transport aircraft at transonic flight in cruise phase is illustrated as the standard L/D mode. The required flight data of monitored flight is requested to eliminate the biases through compatibility checks. The flight data of study case in the present study is also illustrated as monitored flight data.Practical implicationsTo diagnose the irregular deviations of flight control surface deflected angles with contributing to the L/D deficiency estimation is an innovation to improve the flight data analysis of FOQA program for airlines. If the irregular deviation problems of control surfaces can be fixed after rigging in maintenance, the goal of flight safety and aviation fuel saving will be achieved.Social implicationsThe flight control surface rigging of unscheduled maintenance is not expected to coincide with an airline’s peak season or unavailable space in hangar. The optimal time of unscheduled maintenance for the flight control rigging will be easily decided through the correlations between excessive fuel cost and flight safety.Originality/valueThis method can be used to assist airlines to monitor irregular angular positions of flight control surfaces as a complementary tool for management to improve aviation safety, operation and operational efficiency.

Green bond as a new determinant of sustainable green financing, energy efficiency investment, and economic growth: a global perspective.

The purpose of the study is to test the role of green bond financing on energy efficiency investment and economic growth. To achieve the study objective, fuzzy decision-making modeling technique is applied. The results revealed that bank loans are now the main source of financing for energy efficiency projects. Project-based financing might be replaced with Energy Performance Contracts (EPC) warranting energy efficiency investment. Moreover, green banks invest both public and private funds in energy efficiency promoting economic growth. The usage of green bonds for financing environmentally beneficial projects or companies is limitless. Providing for screening energy efficiency investment proposals with small payback hurdle rates might have large opportunity costs. Green bonds can be used to remove the financing barriers for green finance and sustainability tool. On this, study provides policy implications to key stakeholders; if suggested policy suggestions implemented successfully, these would help to enhance scope of green bond financing to uplift energy efficiency financing and green growth successfully.

Fuzzy observer-based fault detection for wind turbines using the finite-frequency approach

ABSTRACT This research investigates the synthesis of a new Finite Frequency Observer (FFO) to detect faults affecting wind turbines. First, the wind turbine nonlinear behaviour is approximated using Takagi-Sugeno (T-S) fuzzy modelling technique for observer design purposes. Then, through the H-/H∞ optimisation technique with Finite Frequency (FF) specification, the FFO design conditions are formulated using the Lyapunov method as a set of LMIs. Finally, to assess the efficiency of the FFO-based detection approach, simulations results are applied to a dynamic 4.8 MW WT model, where an Unknown Input Observer method is used for comparison.

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Robotic manipulators are widely used in applications that require fast and precise motion. Such devices, however, are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part. To address these issues, the Linear Matrix Inequalities (LMIs) and Parallel Distributed Compensation (PDC) approaches are implemented in the Takagy-Sugeno Fuzzy Model (T-SFM). We propose the following methodology; initially, the state space equations of the nonlinear manipulator model are derived. Next, a Takagy-Sugeno Fuzzy Model (T-SFM) technique is used for linearizing the state space equations of the nonlinear manipulator. The T-SFM controller is developed using the Parallel Distributed Compensation (PDC) method. The prime concept of the designed controller is to compensate for all the fuzzy rules. Furthermore, the Linear Matrix Inequalities (LMIs) are applied to generate adequate cases to ensure stability and control. Convex programming methods are applied to solve the developed LMIs problems. Simulations developed for the proposed model show that the proposed controller stabilized the system with zero tracking error in less than 1.5 s.

OWA fuzzy regression

In this paper, the Ordered Weighted Averaging (OWA) operators will be considered to propose general fuzzy regression modeling technique for crisp/fuzzy-input fuzzy-output data. Here, by considering various kinds of a special form of OWA operators we will be able to1.investigate different approaches for aggregation of residuals/errors,2.construct a multi-objective optimization problem,3.provide high-breakdown estimators,4.identify poorly fitted data points, which might be considered as outliers while limiting their effects on the model,5.show how robust alternatives to other fuzzy regression models can be obtained as a result. Finally, a special case of the OWA-based fuzzy regression model known as OWA-Least Trimmed Absolutes Deviations (OWA-LTAD) fuzzy regression model is studied in details for crisp-input fuzzy-output data. An algorithm is investigated in the present study for estimation process in which1.the highest breakdown value will always be achieved (up to the maximum of 0.5) for problems with multiple outliers,2.the poorly fitted dataset can be labeled as outliers in an elemental set. In the presence of multiple outliers, the new method is particularly useful, and the performance of the proposed approach is illustrated for modeling several simulated datasets and a real valued dataset all contaminated with multiple outliers as well. We will look at an application of the proposed approach, in particular, to detect outliers in such models.

Determinations of 3D ease allowance in a virtual environment for customized garment design using fuzzy modelling

The relationship between garment design parameters, wearer morphology, posture, fabric properties and ease allowance values is very complex and uncertain due to the presence existence of various human factors related to designer and consumer perception. The aim of this work is to set up a series of mathematical models to characterize and determine the relationship between these parameters. For this purpose, fuzzy modelling techniques were proposed to extract IF-THEN rules by learning from the experimental data. First, the general modelling principle and related concepts were outlined. The different samples collected to obtain the learning data were described in detail. The samples were then characterized by instrumental measurements and sensory evaluations to form the input and output data for modelling. Based on the obtained input and output learning data, it became possible to determine the modelling procedure to extract relevant fuzzy rules. The computation then helps in determining the 3D ease allowance values from the desired human perception on fit, comfort and other design parameters. For a new sample and consumer morphology, the relevant degree of input data with respect to all the fuzzy rules was also calculated to find the most appropriate ease allowance value. The developed mathematical model followed by experiments also provides advantages in controlling and adjusting patterns according to perceptions of the designer and consumer. The concept of determining 3D ease allowance could be further integrated into the 3D clothing CAD approach to obtain a suitable garment surface on specific virtual human model environments to customize any garments in the fashion industry.