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In the era of common digitalization and far reaching progress in the field of cybernetics, it is necessary to use the knowledge and experience in military cybernetics applications. In the field of machines, control fuzzy expert inference systems open new horizons and possibilities. Generally, the main affect of human efforts in the case of artificial intelligence is to create a machine with a set of behaviors and attitudes that would allow it to work independently, with ability to adjust to changing environmental conditions and an advisory role in the decision-making process. It should be noted that this technology used in some cases has already produced successful results. This paper aims to describe how the fuzzy expert inference membership function shapes influence analysis on selected air tasks efficiency evaluation results. Presented results prove that proper fuzzy membership functions shape selection has fundamental influence on aircraft system level of efficiency evaluation (its calculation accuracy). Using this technology in military aviation air tasks efficiency evaluation aspects is pioneer.

In the era of common digitalization and far reaching progress in the field of cybernetics, it is necessary to use the knowledge and experience in the domain of cybernetics in military applications. Artificial intelligence that so much influences on the imagination of scholars actually opens new horizons when it comes to control the machines. Relatively recently, it is introduced for military applications such departments of artificial intelligence as fuzzy logic, expert systems and fuzzy control theory.

In this paper, fuzzy expert inference membership function shapes influence analysis on selected air tasks efficiency evaluation results are described. Presented results prove that proper fuzzy membership functions shape selection has fundamental influence on aircraft system level of efficiency evaluation (its calculation accuracy).

The issue solved in the paper is based on application of theoretical results in practice. The paper can be estimated to bridge the gap between theory and practice in specific field.

Using this technology in military aviation air tasks efficiency evaluation aspects is pioneer.

This paper aims to deal with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems.

It is well-known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system.

An effective approach is proposed to control chaos via the combination of fuzzy controllers, fuzzy observers and dithers.

If a fuzzy controller and a fuzzy observer cannot stabilize the chaotic system, a dither, as an auxiliary of the controller and the observer, is simultaneously introduced to asymptotically stabilize the chaotic system.

Thus, the behavior of the closed-loop dithered chaotic system can be rigorously predicted by establishing that of the closed-loop fuzzy relaxed system.

This project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.

A fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.

The proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.

The paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.

Inventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.

The purpose of this paper is to propose a stability analysis and control synthesis for achieving passivity properties of a class of continuous‐time nonlinear systems. These nonlinear systems are represented via continuous affine Takagi‐Sugeno (T‐S) fuzzy models, which played an important role in nonlinear control systems. The affine T‐S fuzzy models are more approximate than homogeneous T‐S fuzzy models for modeling nonlinear systems. Using the energy concept of passivity theory with Lyapunov function, the conditions are derived to ensure the passivity and stability of nonlinear systems. Based on the parallel distribution compensation (PDC) technique, this paper proposes a fuzzy controller design approach to achieve the passivity and stability for the continuous affine T‐S fuzzy systems.

For solving stability and stabilization problems of affine T‐S fuzzy models, the conversion techniques and passive theory are employed to derive the stability conditions. By applying the linear matrix inequality technique, a modified iterative linear matrix inequality algorithm is proposed to determine and update the auxiliary variables for finding feasible solutions of these stability conditions.

By studying the numerical example, the proposed design technique of this paper is an effectiveness and useful approach to design the PDC‐based fuzzy controller. From the simulation results, the considered nonlinear system with external disturbances driven by proposed design fuzzy controller is stable and strictly input passive.

This paper is interesting for designing fuzzy controller to guarantee the stability and strict input passivity of affine T‐S fuzzy models.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

The purpose of this paper is to analyze the fuzzy reliability of the compressor house unit (CHU) system in a coal fired thermal power plant under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process. This paper uses different fuzzy membership functions to quantify uncertainty and access the system reliability in terms of different fuzzy reliability indices having symmetric shapes.

This study analyses the fuzzy reliability of the CHU system in a coal fired thermal power plant using T_ω-based generalized fuzzy Lambda-Tau (TBGFLT) technique. This approach applies fault tree, Lambda-Tau method, different fuzzy membership functions and α-cut coupled T_ω-based approximate arithmetic operations to compute various reliability parameters (such as failure rate, repair time, mean time between failures, expected number of failures, availability and reliability) of the system. The effectiveness of TBGFLT technique has been demonstrated by comparing the results with results obtained from four different existing techniques. Moreover, this paper applies the extended Tanaka et al. (1983) approach to rank the critical components of the system when different membership functions are used.

The adopted TBGFLT technique in the present study improves the shortcomings of the existing approaches by reducing the accumulating phenomenon of fuzziness, accelerating the computation process and getting symmetric shapes for computed reliability parameters when different membership functions are used to quantify data uncertainty.

In existing fuzzy reliability techniques which are developed for repairable systems either triangular fuzzy numbers, triangle vague sets or triangle intuitionistic fuzzy sets have been used for quantifying uncertainty. These approaches do not examine the systems for components with different membership functions. The present study is an effort in this direction and evaluates the fuzzy reliability of the CHU system in a coal fired thermal power plant for components with different membership functions. This is the main contribution of the paper.

The purpose of this paper is to investigate the stabilization of unstable periodic orbits of Chua’s system using adaptive fuzzy sliding mode controllers with moving surface.

For this aim, the sliding mode controller and fuzzy systems are combined to achieve the stabilization. Then, the authors propose a moving sliding surface to improve robustness against uncertainties during the reaching phase, parameter variations and extraneous disturbances.

Afterward, the authors design a sliding observer to estimate the unmeasurable states which are used in the previously designed controller.

Numerical results are provided to show the effectiveness and robustness of the proposed method.

Fuzzy-based metaheuristic algorithm is used to optimize the fuzzy controllers for the nonlinear level control system subject to uncertainty specially in the main actuator that has lost effectiveness (LOE). To optimize the fuzzy controller, type-1 harmonic search (HS) and interval type-2 (HS) will be used.

The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for Fault-Tolerant Control (FTC) applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has lost effectiveness (LOE) and also the same controller will be tested on DC motor angular position control with and without noise.

The key contribution of this work is the discovery of the best approach for generating an optimal vector of values for the fuzzy controller's membership function optimization. This is done in order to improve the controller's performance, bringing the process value of the two-tank level control process closer to the target process value (set point). It is worth noting that the type-1 fuzzy controller that has been optimized is an interval type-2 fuzzy system, which can handle more uncertainty than a type-1 fuzzy system.

The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for FTC applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has LOE will be tested on DC motor angular position control with noise. Two nonlinear uncertain processes are used to demonstrate the effectiveness of the proposed control scheme.

This paper aims to design and develop a learning‐based fuzzy colour prediction system for providing more effective apparel design in computer‐aided design system.

In this study, we propose using a fuzzy system integrated with preliminary knowledge of colour prediction for facilitating apparel design. The performance of the proposed system is evaluated in terms of its computational efficiency and robustness. In addition, the proposed system is evaluated by target group of customers.

It was found that the performance of the proposed system is better than the traditional approach.

Although the proposed system has some limitations, the outcome of this study could be used to produce a future breakthrough in providing an intelligent computer‐aided design system for apparel product.

Using such an approach, an apparel designer could predict the favourite colours of garment for a target group of customers. The system uses preliminary knowledge about the customers' profiles and evaluations. Such fuzzy approach for colour prediction is established, which is not used in a traditional way in apparel design.

This paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.

In this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.

The finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.

The developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.