DC machine usage in Electric Vehicles (EVs) has been gaining a notable amount of focus. The speed of DC motor-driven wheels in an EV changes as it encounters disturbances like a reduction in tire air volume, or a corrugated and rugged surface on which it is driven. This continuous disturbance and variation in speed could result in the exertion of EV circuits, which can be fatal for passengers. Hence, a control method that could respond to the disturbance and give a signal to the motors of the wheels for automatic speed control is required. Thus, this paper proposes artificial neural network (ANN)-based control strategies for enhanced speed regulation in DC motor-driven electric vehicles. This paper highlights the focus on ANN control strategies in the context of DC motors of EVs. Different ANN architectures such as radial bias network (RBNN), probabilistic neural network (PNN), feed-forward network (FFNN), Elman network, NARX network, NAR network, and recurrent neural network (RNN) are implemented to design the gains of the PID (Proportional–Integral–Derivative) control loop of the DC motor. A thorough analysis concerning different activation functions, mean squared error, mean absolute error, and weight-bias functions is provided. The efficacy of all these methods is tested when the EV system is subjected to key disturbances, namely, step, ramp, sinusoidal, and chirp. System responses under all these test conditions for all the ANN architectures are drawn. A better ANN architecture to tune the PID controller is recommended based on these transient characteristics and disturbance rejection ability. From the results, it is observed that the performance of FFNN is superior to that of other ANNs due to its shorter rise time, less peak overshoot, lower delay time, and lower steady-state error. Thus the proposed work leverages the usefulness of ANNs to achieve more precise speed control, enhancing the overall performance of EVs.

The article examines the application of the DDACMD technique for analyzing electrical current signals from induction motors to classify their operational conditions. Specifically, the study focuses on categorizing the motor into three distinct states: healthy motor, motor with one broken bar, and motor with two broken bars. To achieve this, the research involves collecting electrical current signals from the motor under various operating conditions. These signals are then processed using DDACMD, a technique designed to extract and analyze distinctive features related to each condition. The processed data are evaluated using classification algorithms that interpret these features to accurately determine the motor's condition. The results of the analysis demonstrate that DDACMD is highly effective in distinguishing between the different motor conditions with a high level of accuracy. This effectiveness highlights the technique's potential for supporting predictive maintenance strategies, allowing for early detection of faults and thereby reducing costs associated with unexpected motor downtimes. The study concludes that DDACMD provides a reliable and precise means of diagnosing faults in induction motors. Its ability to accurately classify motor conditions makes it a valuable tool for enhancing maintenance practices. The article also suggests that further research should explore the broader applications of DDACMD in various fault detection scenarios and different types of machinery to fully leverage its diagnostic capabilities. This could significantly improve preventive maintenance efforts and operational efficiency across diverse industrial settings.

Ground-Penetrating Radar (GPR) systems with ultra-wideband (UWB) antennas introduce the benefits of both high and low frequencies. Higher frequencies offer finer spatial resolution, enabling the detection of small-scale features and details, while lower frequencies improve depth penetration by minimising signal attenuation, allowing the system to explore deeper subsurface layers. This combination optimises the performance of GPR systems by balancing the need for detailed imaging with the requirement for deeper penetration. This work presents the design of a wideband inverted U-shaped patch antenna with a wide rectangular slot centred at a frequency of 1.5 GHz. The antenna is fed through a microstrip feed line and employs a partial ground plane. Through simulation, the antenna is optimised by varying the patch dimensions and slot size. Further modifications to the partial ground plane improve UWB and gain characteristics of the antenna. The optimised antenna is fabricated using a double-sided copper clad FR4 substrate with a thickness of 1.6 mm and characterised using a Vector Network Analyser (VNA), with a final dimensions of 200 mm x 300 mm. The experimental results demonstrate a return loss below -10 dB across the operational band from 1.068 GHz to 4 GHz and achieve a maximum gain of 7.29 dB at 4 GHz. In addition to other bands, the antenna exhibits a return loss consistently below -20 dB in the frequency range of 1.367 GHz to 1.675 GHz. These results confirm the antenna’s UWB performance and its suitability for GPR applications in utility mapping, landmine and artefact detection, and identifying architectural defects.

In the context of grid-connected PV-EV charging stations, efficient power management is a crucial issue. However, existing approaches often rely on the stability of the electrical grid, which can be disrupted by grid faults, causing EV charging interruptions. Moreover, neglecting real-time adjustments and battery electric vehicle (BEV) state of charge can lead to battery damage due to over-current or overvoltage situations, regardless of weather conditions. To address these limitations, a novel station manager algorithm is proposed, which dynamically adjusts power flow among the PV system, EV power demand, and the grid based on real-time measurements of system powers, grid availability, and BEV state of charge. This dynamic adjustment ensures an uninterrupted power supply to the EV while maintaining its battery safe during the charging operation. The proposed station manager introduces multiple operating modes, including adaptive charging mode and fast charging mode, each integrated with a dedicated model predictive controller (MPC) to achieve its specific control objective. Through a semi-experimental simulation using a process-in-the-loop (PIL) test approach on an embedded board, the eZdsp TMS320F28335, the results demonstrate the effectiveness of the algorithm in balancing power flow between the PV power and the BEV, optimizing energy utilization, and ensuring uninterrupted and reliable power supply to the EV.

This paper presents an innovative approach to photovoltaic (PV) grid integration using a single three-level neutral-point clamped (3L-NPC) converter with an advanced control employing a nonlinear control technique and Phase Disposition Pulse Width Modulation (PD PWM). Unlike conventional systems that utilize separate DC-DC and DC-AC converters, this study proposes a unified control strategy to regulate DC voltage and ensure unitary power factor correction (PFC) at the grid side. The DC voltage reference is generated by a Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm, optimizing the extraction of maximum power from the PV array.

The methodology includes detailed modeling of the PV system, the 3L-NPC converter, and the proposed control algorithms. The PD PWM technique is implemented to improve the harmonic performance and voltage balancing of the converter. Simulation results, conducted in MATLAB/Simulink, demonstrate the effectiveness of the proposed control strategy in maintaining stable DC voltage and achieving unitary PFC under varying solar irradiance. The results show significant efficiency improvements and reduced total harmonic distortion (THD) compared to traditional methods. In conclusion, the integration of MPPT and PFC in a single 3L-NPC converter presents a cost-effective and efficient solution for PV grid integration. The proposed system enhances overall performance and reliability, paving the way for more streamlined and sustainable renewable energy systems.

Power electronic converter-based microgrids generally suffer from poor power delivery/handling capability during sudden load changes, especially during islanded operations. This is due to a lack of transient energy support to compensate for sudden load changes. The literature has suggested the use of adaptive droop control to provide compensation during transient conditions, thereby improving their power delivery capability. In this context, fuzzy logic-based adaptive droop control is a state-of-the-art technique that was developed based on empirical knowledge of the system. However, this way of selecting the droop coefficient values without considering mathematical knowledge about the system leads to instability during transient conditions. This problem is further worsened when dominant inductive load changes occur in the system. To address this limitation, this paper proposes an improved fuzzy logic-based adaptive droop control method. In the proposed methodology, the values of droop coefficients that are assigned for different membership functions are selected based on a stability analysis of the microgrid. In this analysis, the feasible range of active power–frequency droop values that could avoid instability during large inductive load changes is identified. Accordingly, infeasible values are avoided in the design of the fuzzy controller. The performances of the proposed andconventional fuzzy logic methods are verified through simulations in MATLAB/Simulink. From the results, it is identified that the proposed method improved the power delivery capability of the microgrid by 14% compared to the conventional method.

Enhancing the control techniques of quadrotors to improve their precision and robustness against wind is crucial for expanding their practical applications and reliability. Quadrotors are increasingly utilized in fields such as aerial surveying, delivery services, disaster response and military operations, where stability and accuracy are paramount. Wind disturbances pose a significant challenge, often compromising the performance and safety of these drones.

This research explores the efficacy of various optimization techniques in enhancing the performance of quadrotor control under wind disturbances. After mathematical modeling of a quadrotor, a backstepping controller is developed for this system and then is optimized by different metaheuristic methods: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Flower Pollination Algorithm (FPA). Each optimization technique is applied to fine-tune the backstepping controller parameters, with the objective of improving the quadrotor's precision, speed, stability, and robustness. Extensive simulations of quadrotor trajectory tracking are conducted to evaluate and compare the performance of these optimized controllers in the presence of wind disturbances.

The results highlight the relative advantages and limitations of each optimization method in terms of response time, overshoot and the deviation rate from the desired trajectory under wind disturbance, providing critical insights into their suitability for enhancing quadrotor control in dynamic and challenging environments.

In modern electric drive systems, both Direct Current (DC) motors and Permanent Magnet Synchronous Motors (PMSMs) are widely used due to their distinct advantages and applications. DC motors are known for their simplicity and ease of control, making them suitable for various applications requiring precise speed regulation. On the other hand, PMSMs offer higher efficiency, better power density, and improved performance, which are crucial for advanced and demanding applications. This paper attempts to apply the Stochastic Fractal Search (SFS) algorithm to optimize the parameters of the PI controller for both DC motor and PMSM engine speed control and then compare their performance in order to determine which motor functions better in terms of this technique. The SFS technique uses the diffusion feature found in random fractals to find the optimal PI values by minimizing the Integral of Time-weighted Absolute Error (ITAE) to improve the performance of both engines. Our study demonstrates significant improvements in speed control stability, overshoot reduction, faster rise times, lower steady-state errors, and quicker settling times, with the overall performance of the PMSM control system being superior to that of the DC motor. These results show the superiority of the SFS algorithm for PMSM compared to DC motor applications.

This paper investigates the application of a multi-objective metaheuristic algorithm, the Multi-Objectives Jellyfish Search (MOJS), to enhance the performance and reliability of Wireless Sensor Networks (WSNs). WSNs, a recent technological advancement, facilitate the strategic deployment of numerous miniature, battery-powered sensors to monitor and gather data from diverse environmental settings. However, the implementation of WSNs faces significant challenges due to limited energy resources. We propose a novel approach, termed WSN-MOJS, which aims to optimize WSN implementation by maximizing coverage and minimizing energy consumption. Simulations were conducted using MATLAB software to design a network consisting of multiple sensor nodes to monitor a designated zone. The process begins by randomly initializing candidate node placements, which are then evaluated using two objective functions as follows: total coverage, and energy expended by the sensor nodes. The MOJS updating process is iteratively applied over multiple iterations. To test the performance of our WSN-MOJS approach, we conducted several simulations by varying the number of nodes, candidate solutions, and iterations. The results indicate that the proposed WSN-MOJS algorithm ensures maximum coverage with an average number of nodes and minimizes energy consumption within a minimal computation complexity due to its exploration and exploitation capabilities. Increasing the number of candidate solutions and iterations significantly improves the Pareto front. Consequently, the non-dominated solutions become well-distributed, and the fitness values are enhanced.

This paper provides a comprehensive analysis of the optical properties of a Zeonex-based porous-core holey fiber (PCHF) operating at 1550 nm. The study looks at critical parameters like dispersion, confinement loss, effective area, nonlinear coefficient, V-number, and bend loss to assess the fiber's performance in optical communication and sensing applications. The aforementioned optical properties are determined using the finite element method, which involves Comsol Multiphysics modeling and simulations. The results indicate that the Zeonex-based PCHF exhibits a significant negative dispersion of -785.7 ps/(nm•km), a confinement loss of 7.098×10^-2 dB/cm, and an effective area of 1.319 µm². The fiber's nonlinear coefficient is measured at 61.46 W^-1 km^-1, with a V-number of 2.21 and a bend loss of 4.939×10^-3 dB/cm. These findings demonstrate the potential of the Zeonex-based PCHF to improve the performance of optical communication systems and sensing technologies. The negative dispersion and low confinement loss indicate that it is suitable for controlling chromatic dispersion and reducing signal attenuation. Furthermore, the effective area and nonlinear coefficient values promote high-power light transmission and efficient nonlinear interactions, whereas the V-number and bend loss parameters demonstrate the fiber's structural robustness and flexibility. Finally, this study emphasizes the promising properties of a Zeonex-based PCHF, arguing for further research and development in advanced photonic applications.