Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Rural regions of Uzbekistan experience continuing issues of energy access because of poor grid networks and variable renewable sources. The solution is small-scale wind turbines and energy storage. But the wind speeds and load demand are variable, and thus this solution needs intelligent control systems to perform its best. This paper is an attempt to design an adaptive neuro-fuzzy inference system (ANFIS) controller to control a small wind power system with a battery storage unit. The controller will be intelligent to control the flow of power between the wind turbine, battery, and local loads. A model of MATLAB/Simulink is created to simulate the reaction of the system to various wind and load conditions. The simulation results indicate that the ANFIS controller improves voltage regulation, reduces power fluctuations, and enhances battery charge–discharge performance compared to the conventional PI controller. Environmental variability is effectively responded to by the system, making it more reliable and energy-efficient. ANFIS control and wind–battery microgrid integration provides a feasible and expandable offgrid electrification solution to remote areas. This strategy promotes the renewable energy ambitions of Uzbekistan and offers an example of smart microgrid implementation in other resource-limited rural areas. The next steps would be towards practical applications and hardware verification....
Group projects are considered a fundamental component of higher education, as they enhance students’ competencies and problem-solving abilities within professional learning environments. Therefore, ensuring student success and providing effective supervision is essential. However, this remains a challenging task due to the reliance on supervisors’ expertise and the diverse characteristics and backgrounds of student groups. In this paper, we introduce a novel theoretical and practical interval type-2 fuzzy logic system (IT2FLS) for early prediction and guidance for novice supervisors by correlating and learning expert supervisors’ assessments according to the required level of support and the risk of failure for student groups needing early intervention. Experimental evaluation was performed based on assessments of 33 graduation projects conducted by expert supervisors, which served as the input–output data for developing interpretable white-box models that allow both novice and expert supervisors to transparently analyse reasoning processes and outcomes. The results demonstrate that the developed IT2FLS predicts the required level of support and the risk of failure for student groups with lower average error and standard deviation, outperforming the encountered Type-1 fuzzy logic systems. This study thus indicates the IT2FLS’s effectiveness in handling linguistic and numerical uncertainties in supervisors’ evaluations of students’ required early interventions....
In this paper, we de7ne fuzzy dual number linear systems (FDLSs) expressed as C̃Z = ̃W, where C represents a crisp dual number matrix and ̃W denotes a fuzzy dual number vector. Our study focuses on three primary objectives. First, we obtain the existence of strong fuzzy solutions to the FDLS by analyzing the CMP inverse of coe:cient matrix. Second, we investigate the existence of nonnegative CMP inverse by analyzing their block structure. Third, we derive general strong fuzzy solutions for the FDLS and develop a corresponding computational algorithm based on the CMP inverse. Meanwhile, a method for obtaining general solutions of the FDLS is proposed under the premise of a strong fuzzy solution. To illustrate our theoretical framework, we present numerical examples demonstrating the e:cacy of the proposed methodology....
This paper introduces a control strategy for a synchronous generator in a wind energy system using an adaptive neuro-fuzzy approach. The suggested controller, based on neuro-fuzzy logic (NFLC), is meant to govern a permanent magnet synchronous generator (PMSG) often utilized in wind power applications. The generator's output voltage phase, phase current, reactive power, active power, angular velocity, and DC voltage are all under control. The adaptive neuro-fuzzy controller efficiently stabilizes all variables in a brief amount of time, according to simulation results. The effectiveness and robust performance of the suggested control system are verified by a number of simulated scenarios. The resilience of fuzzy logic control (FLC) and NFLC systems was compared. The study carefully tested the performance of both control techniques under varied operating settings and disturbance situations to determine their relative stability, flexibility, and efficacy in sustaining desired system behavior....
This paper studies the problem of controller design for polynomial fuzzy-model-based (PFMB) systems. To make full use of the information of membership functions (MFs), the operating space is partitioned into several subspaces. According to the information of partitions, switched state feedback and output feedback controllers are designed, respectively, for the system. By employing the approximated membership function method and a new relaxation technique, relaxed stabilization criteria in the form of the sum of squares are derived without the need to impose any constraints on system matrices. Simulation examples are provided to illustrate the validity of the presented method....
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