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The use of Fuzzy Logic Controller (FLC) as a speed controller for Induction Motor (IM) drives is garnering strong researchers' interest since it has proven to achieve superior performance compared to conventional controllers. The aim of this study is to review and investigate the design, operations, and effects of rules reduction for FLC in IM drives. Based on the literature, the most commonly used technique to design FLC Membership Functions (MFs) rule-base and control model is based on engineering skills and experienced behavioral aspects of the controlled system. Simplified fuzzy rules approaches have been introduced to reduce the number of fuzzy rules in order to realize hardware implementation. This study discusses different simplified rules methods applied to IM drives. Most of the proposed methods shared a common drawback in that they lacked systematic procedures for designing FLC rule base. Therefore, this research proposed a methodological approach to designing and simplifying the FLC rule-base for IM drives based on dynamic step response and phase plane trajectory of the second order representation of IM drives systems. The proposed method presents guidance for designing FLC rule-base based on the general dynamic step response of the controlled system. Following the proposed method procedures, a (9, 25, 49) rules size has been designed and simplified to a (5, 7, 9) rules size. The effectiveness and accuracy of the designed rules as well as the simplified rules were verified by conducting simulation analysis of IM drives using MATLAB/Simulink environment. Step speed command performance comparisons were achieved with both standard designed and simplified rules at various speed demands. The simulation results showed that the simplified rules maintain the drive performance and produced similar behavior as the standard designed rules.

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices. The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections. Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices. The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form. The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes. The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time.INDEX TERMS Internet-of-things, federated learning, energy consumption, edge nodes, central processing unit.

<span>An inverter system applied with the PV source typically has a problem of lower input voltage due to constraint in the PV strings connection. As a countermeasure a DC-DC boost converter is placed in between to achieve a higher voltage at the inverter DC link for connection to the grid and to realize the MPPT operation. This additional stage contributes to losses and complexity in control thus reducing the overall system efficiency. This work discussed on the design and development of a grid-connected quasi-Z-source PV inverter which has different topology and control method compared to the conventional voltage source inverter and able to overcome the above disadvantages. Modelling and performance analysis of the voltage and current controller to achieve a good power transfer from the PV source, as well sycnchronization with the grid are presented in detail. Results from both simulation and experimental verification demonstrate the designed and developed grid-connected qZSI PV inverter works successfully equivalent to the conventional voltage source inverter system.</span>

This paper presents the implementation of two-arm modulation (TAM) technique for the independent control of a two-induction motor drive fed by a five-leg inverter (FLI). A carrier-based space vector pulse width modulation technique for TAM is proposed to generate switching signals for FLI. Two independent three-phase space vector modulators are utilized to control two motors. The motor drive system applies two separate indirect field-oriented control methods. The stationary voltage outputs from the vector control are synthesized in the three-phase space vector modulator to generate switching signals for FLI. The performance of the independent control of the motors and the voltage utilization factor are likewise analyzed. Simulation and experimental results verify the effectiveness of the proposed method for the independent control of the two-motor drive system. The proposed technique is successfully validated by dSPACE DS1103 experimental work.