Engineering and Technology Journal

In this paper, the application of Sliding Mode Control (SMC) and its integration with Finite-Time Stability (FTS) for controlling 1-degree of-freedom (DOF) system has been explored. By combining FTS with SMC, the convergence speed is significantly improved, enhancing the system's resistance to external disturbances. This paper demonstrates a detailed comparative study on the application of SMC based on FTS with a SMC, and with conventional-PID (C-PID). The SMC developed in this paper is based on a PID controller to ensure that the system's trajectory converges exponentially to zero. The results show that using SMC with FTS provides faster and more reliable system stability compared to the other two methods. The comparison also includes the utilization of different functions in the developed SMC, such as sign, tan, and saturation functions. The effects of disturbance and uncertainty have been considered as well. The results show that using SMC with FTS significantly outperforms SMC without FTS and the C-PID in all cases; the achieved Settling Time of SMC with FTS is approximately 1.5 seconds, compared to 4 seconds for the Conventional PID (C-PID) controller. This represents a 62.5% improvement in response speed. The reduction in Overshoot for the C-PID controller is around 15%, while using SMC with an FTS controller reduces the overshoot value to just 3%, indicating an 80% improvement in overshoot and demonstrating enhanced stability and performance

This study concentrated on utilizing a neural network controller and inductive power transfer to enhance the electric vehicle charging. Electric vehicles are a key transportation system component since they lower fuel costs, noise levels, and carbon emissions. The charging duration and operational battery efficiency are the primary obstacles to wireless charging in electric vehicles. In the beginning, the system was designed. It included the primary side (grid, rectifier, DC-DC boost converter, and DC-AC high-frequency inverter), mutual inductance (one coil transmitter and six coils receiver), and secondary side (single-phase rectifier and battery pack). A neural network controller has been employed to control the current and voltage to get fast charging. The simulation design and control have been simulated using MATLAB 2022b. As a result, the DC-DC boost converter raised the transferred voltage to 800 V within 20 KHz to reduce the wave signal ripple. In comparison, a neural network controller kept the transfer voltage and constant current to 800 V and 100 A during charging from 10% to 100%. Moreover, a neural network controller worked on charging the system within 15 minutes as a fast-charging technique. Also, the behavior and value of the voltage transmitted to the battery have been verified

This paper presents a practical investigation into the design and implementation of a multi-stage controller, which integrates a Model Reference Adaptive Controller (MRAC) with a Proportional-Integral-Derivative (PID) controller for trajectory tracking control in a Four Mecanum Wheels Mobile Robot (FMWMR). The MRAC adaptively updates the control parameters based on the difference between the actual and desired behaviors of the mobile robot. In contrast, the PID controller manages the angular velocities of the wheels. The mobile robot has been designed and constructed with two holders to facilitate the transportation of logistical items and constrain the weight to the center of the mobile robot to reduce the effect of the inertia force. The distinctive motion characteristics of the FMWMR are elucidated by explaining its kinematic and dynamic models. The control signals for the FMWMR motors are derived from the output of the multi-stage controller. The mobile robot operates through serial communication between MATLAB/Simulink and Arduino hardware. Experimental results demonstrating the control of the (FMWMR) by the proposed controller as it follows a Sine-Spiral trajectory are presented, highlighting the performance of the multi-stage controller monitoring the behavior of velocities and motor torques and illustrating the real-time adaptive variation of the MRAC control parameters.

Automatic load frequency control (ALFC) is an important part of auxiliary services in power systems. To sustain system accuracy and ensure the safety of power systems, ALFC system design is a vital issue. Power system networks must quickly address the imbalance between generation and demand, or else the power line frequency will depart from its nominal value. However, performance might be disappointing, and frequency deviation can occur when a substantial disturbance occurs, such as when a rapid, significant load change happens. In particular, an enormous number one- and two-dimensional lookup table is used; in this study, the 1-D Lookup Table (1-DLuT) method is generalized to design LFC in single area and two areas power system. The proposed controller reduces the effects of load disturbance in the frequency fluctuations and contributes to the stabilization of the power system. In PID controller design methods, the most common performance criteria are integrated absolute error (IAE), the integration of time weight square error (ITSE), and the integration of squared error (ISE), which can be evaluated analytically in the frequency domain. A common performance criterion for control system design is the Integral of Time multiplied by the Absolute Error (ITAE) index, which is used to minimize the integral of the time absolute error performance index. Finally, the proposed 1-DLuT-based interpolation algorithm is compared with two different controllers, including a PI and an ANN. In other words, the simulation results showed that the optimal proposed system performs better within different variations of the stated loads

Smart actuation has transformed mechatronics over the past six years by harnessing materials that turn heat, electricity, magnetism, or light into finely tuned motion. Our survey of developments from 2019 to 2025 reveals clear trade-offs: electro-active polymers can stretch beyond 200% but operate at roughly 30% efficiency, whereas piezoelectric devices achieve over 95% efficiency with displacements under 0.1%. Ionic polymer–metal composites and magnetorheological actuators strike the best balance for precision work, delivering about 50% stroke at 60% efficiency. Real-world examples show that packing tiny shape-memory alloy bundles into soft exosuits grants sub-millimeter accuracy without sacrificing comfort, and that materials capable of reversing energy flow and monitoring their state greatly improve closed-loop control and fault detection. We examine control methods—from model-predictive to adaptive schemes alongside scalable fabrication routes spanning 3D (Three-Dimensional) printing to microfabrication, and identify lingering challenges in manufacturing scale-up and high-frequency response. Looking forward, embedding computation within actuator materials, adding artificial intelligence-driven adaptability, combining multiple stimuli in hybrid designs, and harvesting energy internally promise to usher in truly autonomous, life like mechatronic systems. This review outlines recommendations to advance energy-efficient, sustainable actuator designs. It highlights the importance of multidisciplinary collaboration among materials engineers, system designers, and control experts to accelerate real-world deployment

Wireless Sensor Networks (WSNs) have become essential in the monitoring and managing environmental disasters, providing real-time data collection and transmission functionalities. Their application is especially crucial in flood-prone areas, where prompt detection and action can reduce substantial human, economic, and infrastructural losses. Wireless Sensor Networks (WSNs) comprise spatially distributed sensors that monitor environmental parameters, including water levels, temperature, and humidity, relaying data to a centralized system for analysis and response. This paper proposes a flood monitoring system based on a wireless sensor network, emphasizing installing water-level sensors in dam basements. The system employs the ZigBee communication protocol to provide low-power, wireless data transmission between sensor nodes and a base station (BS), which subsequently transmits information to a mobile device through Internet of Things (IoT) integration. This approach is significant due to its capacity for continuous, automated monitoring and early warning of abnormal water levels. When the water level surpasses a specified threshold (26 °C, utilized here as an indicator of water height in the sensor design), the system activates an audio alarm via a buzzer to notify adjacent personnel of potential flooding hazards. The temperature-based surrogate threshold is adjusted according to the sensor's sensitivity and the dam's structural safety restrictions. Experimental findings illustrate the system's efficacy in precisely detecting and conveying real-time variations in water levels. Furthermore, a comparative study demonstrates that the ZigBee-enabled system provides enhanced energy efficiency, scalability, and transmission range compared to previous technologies employed for analogous applications. This research implies that catastrophe resilience can be improved by integrating IoT and WSN technologies, hence facilitating proactive infrastructure management and enhancing community safety in at-risk areas

This study investigates the laser cutting of stainless steel 201 using the Response Surface Methodology with a 32-run experimental design (L32). The responses are top and bottom kerf width, while laser power, cutting speed, frequency, focal position, and gas pressure are selected as input process parameters. A comprehensive statistical analysis, including analysis of variance, main effect plots, residual plots, and interaction plots, was conducted to assess the significance and contribution of each parameter. The ANOVA results for TKW and BKW confirmed the statistical significance of all machining variables. For BKW, laser power had the highest influence (72%), followed by focal position (17%), frequency (8%), gas pressure (2%), and cutting speed (1%). Similarly, for TKW, laser power contributed the most (61%), followed by focal position (19.8%), gas pressure (4.5%), cutting speed (3.8%), and frequency (2.7%). The findings highlight the dominant role of laser power and focal position in determining kerf quality. This study's main contribution is pinpointing the ideal laser cutting parameters for reducing kerf width, which improves precision and efficiency in stainless steel 201 cutting. The findings serve as a valuable reference point for enhancing laser-cutting processes in industrial applications.

This study presents an innovative self-tuning system for first-class RC filter circuits, specially designed to achieve a target cut-off frequency of 500 kHz with unprecedented accuracy and high energy efficiency. The proposed model is based on a multivariate adaptive tuning algorithm that synchronously adjusts both the resistance (R) and the capacitance (C). The effectiveness of the model was verified through a three-level methodology that included theoretical modeling using Maxwell's equations, digital simulation using the MATLAB/Simulink environment, and practical testing with an accurate spectrometer. The results demonstrated a standard frequency accuracy of 99.9969% and a relative error of less than 0.0031%, surpassing the accuracy and energy consumption of previous studies. It also recorded a low power consumption of 785.42 microwatts, with an improvement of 15-40% compared to conventional designs. The system achieved rapid convergence in less than five iterations, three times faster than traditional algorithms, as well as superior thermal stability of ±0.001%/°C in the range of -20 to +70 °C. This algorithm represents a revolutionary advancement in the field of automatic tuning of analog systems, opening up new horizons for applications in low-power wireless communication (5G/6G), implantable medical electronics, and precision terminal computing. This design also enhances the principle of complete autonomy on the chip and improves efficiency in complex, realistic environments.

This study investigates the finite element analysis (FEA) of a surface-mounted permanent magnet motor (SMPMM) using QuickField software. The model was developed by solving Maxwell’s equations on meshed SMPMM geometries, integrating real B-H curve data for laminated steel stator and rotor materials to accurately represent the nonlinear behaviour of the SMPMM. The simulation was performed to evaluate the magnetic flux distribution, air-gap flux density, torque, and self-and mutual inductance at an operational speed of 3000 rpm, and compares the simulated outputs with existing experimental results. Under current excitation up to 2 A, the simulated air gap density ranged from 0.7 Tesla (T) at minimum rotor-stator coupling to 1.2 T at peak alignment during an electrical cycle. Peak torque reached 1.8823 N.m at a 0.45 mm air gap, decreasing slightly to 1.8572 N.m at 0.75 mm. Self-inductance declined from 0.8 H to 0.5 H, while mutual inductance declined from 0.049 H to 0.03 H, showing the effect of magnetic saturation. Core and eddy current losses increased nonlinearly at higher speeds and flux densities. The validated results highlight the importance of incorporating nonlinear magnetic properties and velocity-dependent losses in SMPMM design accurate prediction of performance under both nominal and extreme conditions, supporting robust, high-efficiency motor development for industrial, automotive, and renewable energy applications