Journal of Measurement, Control, and Automation
The Journal of Measurement, Control and Automation, formerly known as the Journal of Measurement, Control and Automation, has been responsible for publishing by the Journal of Automation Today since 2013. From August 2024, according to the new Operating License granted by the Ministry of Information and Communications, the Journal has been renamed the Journal of Measurement, Control and Automation. The Journal also has an independent ISSN index of 3030-4555. The Journal is currently recognized by the Interdisciplinary Council of Electricity - Electronics and Automation up to 1 point; the Council of Mechanics up to 0.5 point The Nafosted Foundation has included it in the list of prestigious scientific journals publishing research works In 2025, the Journal will join Crosreff and start assigning DOIs to articles.
Stability control of a double inverted pendulum using a decoupled backstepping slidingmode strategy
The double inverted pendulum on a cart (DIPC) is a classical benchmark in nonlinear control due to its high instability, underactuated structure, and complex dynamic behavior. Traditional control approaches, such as Linear Quadratic Regulators (LQR) or conventional Sliding Mode Control (SMC), often face limitations in robustness and dynamic decoupling when applied to such systems. In response to these challenges, this study presents a Decoupled Backstepping Sliding Mode Control (DB-SMC) strategy that integrates the systematic design framework of backstepping with the robustness properties of sliding mode control. By introducing intermediate virtual control surfaces, the DB-SMC method effectively decouples the dynamics of the cart and the two pendulums, simplifying controller design and improving system stability. The control law is derived using Lyapunov stability theory to ensure convergence and robustness in the presence of uncertainties. Simulation results demonstrate that the proposed method achieves fast convergence, reduced overshoot, and strong disturbance rejection compared to traditional methods. The effectiveness of the DB-SMC controller highlights its potential for broader applications in nonlinear and underactuated systems.
Combination of sliding mode control and non-voltage measurement NLM modulation for modular multilevel converter
Modular Multilevel Converter (MMC) is an optimal solution for the advanced power conversion for Medium Voltage (MV) and High Voltage (HV) applications. This paper proposes an enhanced sensorless Nearest Level Modulation (NLM) with a Sliding Mode Controller (SMC) scheme with Integral Sliding Surface for the grid-connected case of the MMC where it stands as a DC/AC inverter. The proposed SMC when applied to MMC shows some outstanding advantages such as fast response, good robustness, and stability under transient conditions. Additionally, the sensorless NLM is proven to be efficient in terms of capacitor voltage balancing while maintaining output voltages tracking the reference voltages fed by the SMC. The simulation results on MATLAB/SIMULINK for the MMC under varying active and reactive power are conducted so as to confirm the correctness and efficiency of the proposed method.
FEM-based modeling and control of 6-phase IPMSM for electric vehicles using JMAG and Matlab Simulink
Research on the modelling and control of an asymmetric six-phase interior permanent magnet synchronous machine (IPMSM) is essential for advancing electric vehicles and high-performance drive systems. This paper presents a modelling and simulation approach for a 100 kW 6-phase IPMSM designed for electric vehicle applications, combining dual dq-axis vector control theory with the Finite Element Method (FEM) using JMAG-Designer to evaluate electromagnetic properties and efficiency. The dual dq model is utilised to analyse flux linkages, inductances, and torque, while FEM addresses nonlinearities, magnetic saturation, harmonics, and asymmetry. The FEM results are integrated into MATLAB/Simulink for validation, enhancing accuracy and ensuring suitability for designing six-phase IPMSMs in electric vehicles, robotics, and intelligent drive systems.
A quadratic DC-DC boost converter with capacitor voltage rating reduction
This paper presents a quadratic DC-DC boost converter topology featuring high voltage gain, common-ground (CG) characteristic, and simplified control with a single active power semiconductor switch. Steady-state analysis and small signal analysis are conducted to model and determine the transfer function of the proposed DC-DC converter. Based on transfer function, the parameters of controllers are determined. The configuration is controlled by two proportional-integral (PI) controllers. The proposed converter are controlled by two closed-loop controllers with feedback references of output voltage and input inductor current. Simulation results are carried out using PSIM software and a 200W experimental prototype with a resistive load to verify the accuracy of the proposed control method.
Chaos detection and control for permanent magnet synchronous motor
Permanent magnet synchronous motor (PMSM) can exhibit chaotic behavior when certain parameters reach critical values. This chaos can significantly impair the performance of the PMSM and potentially jeopardize the safe operation of the power system if not addressed promptly. Since system parameters can change during operation, the PMSM can enter a chaotic state at any moment. Therefore, the first step is determining whether the system is experiencing chaos, followed by implementing control measures to eliminate this chaotic behavior. This paper proposes an online observer designed to detect chaotic behavior as soon as it occurs in the PMSM. Additionally, a chaotic controller based on state feedback is developed to counteract the chaotic behavior. Through mathematical analysis and simulation results, the paper demonstrates that the observer can successfully identify chaos, triggering the chaos controller automatically when such behavior is detected. This process helps stabilize the system quickly.
Design of a service robot for restaurant applications using Lidar-Based SLAM localization
In this study, we propose the design of a table-running robot intended for deployment in restaurant envi-ronments, aiming to automate food service operations while enhancing safety and accuracy. The proposed robot employs a multi-sensor fusion localization method, integrating data from a LIDAR sensor, wheel en-coders, an accelerometer, and a gyroscope. Unlike systems that rely on magnetic tape for navigation, our approach utilizes a SLAM (Simultaneous Localization and Mapping) algorithm to process sensor data and construct a dynamic map of the environment. This enables the robot to navigate flexibly and reroute in re-sponse to moving obstacles. Additionally, we introduce a localization method using V-L markers, allowing the robot to autonomously locate and dock with its charging station when battery levels are low. Experi-mental results confirm the robot's accurate and reliable performance in real-world conditions.
Design of a single-stage converter for wireless charging systems in automated guided vehicles
Currently, wireless charging systems are widely used in various fields. In wireless charging systems for automated guided vehicles, traditional multi-stage structures fail to achieve high efficiency and require significant investment costs. Moreover, these multi-stage structures demand separate and complex control. This paper proposes a single-stage converter based on a totem-pole structure and a phase-shift control method to regulate constant current charging in wireless charging systems for automated guided vehicles. The proposed converter has a simple structure. It eliminates the need for a large-capacitance Clink, achieves soft switching for the semiconductor switches, ensures a high input power factor, and maintains a low total harmonic distortion. Additionally, a dual-side LCC compensation circuit is designed to maximize transfer efficiency. The results show that the overall system efficiency from the grid input to the load exceeds 93,92%, with a high input power factor ranging from 0,960 to 0,989, and a low input THD of less than 1,84% across the entire operating range.
The altitude, position PID controllers design for UAV quadcopter: from theory to experiment
Unmanned aerial vehicles (UAV), of which the four-rotor type, called UAV quadcopter, is increasingly applied in many aspects of socioeconomic life, especially in difficult tasks that humans can hardly perform. UAV quadcopter is a complex multivariate nonlinear object, always affected by air turbulence and wind. This paper presents the process of designing, simulating and testing the position and altitude controller for quadcopter UAV when considering external disturbances, based on the application of PID control law according to the CohenCoon method. Simulation results on Matlab show that the PID control system using the Cohen-Coon method gives good UAV trajectory control quality with very small position-height error, <1%, has better response time to object delay, and is more stable with small load disturbance, compared to the Ziegler-Nichols method. Then, the author conducts testing of the Cohen-Coon PID controller on the F450 quadcopter UAV hardware. The experimental results show that the proposed PID controller ensures accurate control of position, altitude, trajectory tracking and maintains stable flight balance in external disturbance conditions with light winds.
Optimizing neural networks for pneumatic muscle actuator system identification: a cooperative coevolutionary approach
This paper presents a cooperative coevolutionary optimization algorithm to overcome issues in gradient descent-based neural network, such as getting stuck in local minimum and slow convergence. The proposed method combines JAYA and a modified differential evolution (DE) techniques to optimize neural network weights. It works by splitting the population into two subpopulations, each focusing on optimizing different aspects of the network weights. The method's effectiveness is tested on two benchmark nonlinear dynamical systems and compared with existing methods. Results show that the neural network optimized by this approach achieves high accuracy and robustness. Finally, the practical applicability of this method is demonstrated by modeling the pneumatic muscle actuator (PMA) system using experimental data, where the PMA system is made up of Festo's MAS-10 N220 pneumatic artificial muscles and controlled with a DAQ NI 6221 card.
Computation of electrodynamic forces acting on clamping beams of windings in power transformers
In response to the growing demand for electrical load driven by socio-economic development, the national transmission grid is being gradually upgraded. In this context, conventional power transformers with capacities of 3x150 MVA-500kV and 3x200 MVA-500kV are increasingly being replaced by higher-capacity units, most notably the 3×300 MVA transformers. To meet this trend and reduce dependence on imported equipment, the research, design, and manufacturing of power transformer with 3x300 MVA-500kV present a significant technological challenge for the domestic industry and remain a current topic of interest among both national and international researchers. During operation, short-circuit events subject the windings to extremely high electrodynamic forces, posing a risk of severe deformation or insulation failure if not properly designed. In the winding structures, the clamping beam plays a critical role in directly withstanding these forces, preserving the mechanical integrity of the winding, and maintaining insulation stability throughout operation. This paper develops an analytical approach combined with the finite element method (FEM) to compute, analyze and simulate the electrodynamic forces and deformation acting on the clamping beam in supper-high voltage transformers with power ratings up to 300 MVA-500kV under various operating conditions. Accurate assessment of the mechanical behavior during short-circuit events is essential to ensure the structural integrity and safe operation of the equipment.
Robust trajectory-tracking for nonholonomic wheel mobile robots using a PSO-tuned control approach
This paper addresses the trajectory-tracking problem for nonholonomic wheeled mobile robots subjected to model uncertainties and unknown external disturbances. In contrast to existing approaches that concentrate primarily on the kinematic control loop, this paper adopts a hierarchical control structure for the wheel mobile robot, including an outer kinematic loop and an inner dynamic loop. For the dynamic control loop, a dynamic controller combining integral terminal sliding mode control and a disturbance observer is developed to achieve disturbance rejection and chattering reduction. For the kinematic control loop, the kinematic controller is designed for accurate trajectory tracking control. Based on the Lyapunov stability theory, the stability analysis is given for both the disturbance observer and the double-loop tracking controller. Furthermore, the Particle Swarm Optimization algorithm is employed to optimize the control gains, thereby enhancing the overall performance of the control system. The simulation result using MATLAB software is conducted to confirm the effectiveness of the proposed method
Coverage maximization of sensor networks with connectivity constraints in obstacle-filled environment based on nature-inspired algorithms
The application of meta-heuristic algorithms has significant potential in various fields, including wireless sensor networks. In this paper, we utilize two algorithms, the Fruitfly optimization algorithm (FOA) and the Nutcracker optimization algorithm (NOA), to address two critical issues: optimizing coverage and ensuring connectivity in sensor networks. The main contribution of this paper is the application of these algorithms to arbitrary communication radius, independent of predefined connectivity assumptions. Simulation results demonstrate the effectiveness of the proposed methods by comparing with each other and with the two traditional algorithms Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Additionally, this paper simulates the coverage area in an environment with different types of obstacles to showcase the practical flexibility of the algorithms.
An Improved YOLOv8 Model for Fish Classification and Disease Detection
Fish classification and disease detection are crucial for sustainable aquaculture, necessitating accurate and efficient vision models. This study introduces FISH-YOLOV8, an enhanced YOLOv8 variant, incorporating: (1) SPD-Conv for optimized feature extraction and reduced computational load; (2) BiFormer Attention for enhanced small object detection and occlusion management; (3) dynamic IoU-threshold NMS to minimize false positives. This Article states that, evaluated on 15,162 images, FISH-YOLOV8 attains a mAP@50 of 0.990 and a mAP@50:95 of 0.859, outperforming baseline YOLOv8 and advanced models such as YOLOv11, at 45 fps, supports effective real-time aquaculture monitoring.
Leveraging multi-head attention transformer deep neural network architecture for improved wind speed forecasting
Wind energy has great potential for electricity generation, but its variability makes accurate wind speed forecasting essential for efficient integration. This study explores the application of a transformer-based deep learning model for wind speed forecasting. The model features an encoder-decoder architecture with multi-head attention, feed-forward layers, and normalization functions. By leveraging a self-attention mechanism, the transformer model effectively captures temporal dependencies in time series data through weighted relationships among input sequences, leading to improved forecasting accuracy. To evaluate its effectiveness, we collected and pre-processed wind speed data from the Hong Phong 1 wind power plant, cleaned the data by removing outliers and addressed missing values. The processed data was then embedded and added positional encoding to prepare for model input. The model was trained, and its performance was benchmarked against other models, including Long Short-Term Memory, Convolutional Neural Networks, and Artificial Neural Networks. The obtained RMSE is quite low, with 0,26 m/s for single-step forecast, 0,73 m/s for 4-step forecast and 1,70 m/s for 16-step forecast. These results demonstrated that the transformer model achieved superior predictive performance, suggesting it as a powerful alternative to traditional forecasting methods, with significant potential for enhancing the accuracy of wind speed predictions.
Adaptive beamforming for uniform circular arrays
Beamforming in smart antennas is a highly effective technique that utilizes the flexible adjustment of antenna radiation characteristics, including main beam direction, interference nulling, and sidelobe level control. This paper proposes an adaptive beamforming solution based on the hybrid optimization algorithm combining Particle Swarm Optimization Grey Wolf Optimizer (HPSOGWO) and Bat Algorithm (BA) for Uniform Circular Arrays (UCA). The proposed approach demonstrates the ability to place nulls at interference directions and steer the main beam toward the desired direction. The modeling of circular antenna arrays, objective function, and evaluation scenarios will be presented. Additionally, the cumulative distribution function (CDF) and signal-to-noise ratio (SNR) will be addressed to assess system performance.
Constant current charging with dual-phase receiver structure in dynamic wireless charging systems for electric vehicles
Wireless dynamic charging is a solution to address the issues of travel distance and charging time for electric vehicles. A constant current charging mode is generally required to maximize energy transfer to the vehicle’s energy storage system in the shortest time. This paper proposes a new method for designing constant current charging with a dual-phase receiver structure in electric vehicles’ wireless dynamic charging system. Specifically, a dual-sided LCC compensation circuit is proposed to achieve constant current charging, while the dual-phase receiver structure reduces current ripple during charging. Simulation and experimental results indicate that the constant current charging mode is achieved with a small deviation of approximately 3.1%.
Simulation design of a multilevel MMC converter based on mathematical model of electronic components with NLM modulation algorithm
This paper presents the design of mathematical models for the power and control circuits of the MMC multilevel converter. The research focuses on describing all electronic components of the MMC multilevel converter using mathematical formulas that accurately reflect the physical nature and functions of these components. As a result, the MMC multilevel converter can be represented in the form of a mathematical model. This facilitates the development of tools to verify control algorithms for MMC more conveniently, given that constructing experimental models and performing HIL simulations of the MMC multilevel converter is challenging due to the large number of converter modules, which exceeds the capacity of HIL simulation deFvices. The process of formulating mathematical models for the electronic components, the entire power circuit, and the control circuit is carried out and simulated using LabVIEW software, applying the NLM modulation algorithm to generate N+1 voltage levels (where N is the number of submodules in each branch of the MMC). The results are then compared with those from an MMC model simulated in MATLAB software under the same simulation conditions and technical parameters to verify the operation of the MMC converter
Linearly constrained programming with integer variables for conductor size selection in electrical distribution grids considering the impact of electricity price
One major subproblem in planning distribution grids is optimally choosing conductor sizes. This subproblem often employs nonlinearly constrained optimization with integer variables as its modeling approach. Attaining optimality on a global scale is not always guaranteed by the nonlinear optimization formulation. As a means of determining the area of the wire’s section in the distribution grid in the most efficient manner possible, this study suggests a model that is based on linearly constrained optimization with integer variables. While adhering to load flow equations, maximum loading restrictions on branches, voltage magnitude bounds, and investment budget constraints, the goal function is minimizing the cost across the lifetime of the distribution system. Piecewise linearization methods and accurately linearized expressions of multiplying a binary variable by a continuous variable are deployed in order to convert the initially nonlinear model into the suggested optimization model. A programming environment called GAMS and an optimizer called CPLEX are used to evaluate the recommended optimization formulation on a 33-node test distribution network. The evaluation is implemented using diverse scenarios about the price of electricity. The calculation outcomes indicate that the size of conductors is significantly influenced by the electricity price. Furthermore, the conductor size based on the suggested methodology is more cost-effective than the one obtained using the method currently utilized in Vietnam
Load rotation control in crane system using hierarchical sliding mode control combined with particle swarm optimization
This study focuses on controlling the direction of the load in the bridge crane system, aiming to ensure accurate direction control while simultaneously damping the residual oscillation caused by the cable twist. A hierarchical sliding mode control algorithm combined with swarm optimization was proposed to achieve that goal. The stability of the system was proven based on the Lyapunov criterion. An objective function, including the settling time and residual oscillation components, was constructed to determine the controller parameters, and the swarm optimization algorithm was applied to solve the problem of optimizing the objective function. The simulation results showed that the proposed method ensured accurate load direction control with fast residual oscillation damping. Comparisons with the input signal shaping method were also made to demonstrate the superiority of the proposed method.
Design of controllers for the rotary inverted pendulum
The rotary inverted pendulum is a typical nonlinear control system widely studied in the field of automatic control, and is commonly used to test and evaluate various control algorithms. Due to its inherent instability and strong nonlinearity, controlling this system presents significant challenges, requiring effective control strategies to maintain the pendulum in the upright equilibrium position. Numerous control methods have been applied to this system, including classical algorithms such as PID, state feedback, and Linear Quadratic Regulator (LQR), as well as modern approaches like fuzzy control and model predictive control (MPC). This study focuses on the design, simulation, and performance comparison of two primary controllers: LQR and fuzzy PID. The control algorithms are validated through MATLAB Simulink simulations and real-time experiments using the Quanser QUBE-Servo 2 module. Evaluation criteria include stability, adaptability, and sensitivity to disturbances. The results show that the LQR controller delivers high performance with minimal oscillations and a short settling time, while the fuzzy PID controller demonstrates better adaptability to varying environmental conditions and unexpected disturbances.
Load adaptive sliding mode control for 6-degree-of-freedom parallel robot system Stewart platform
The Gough-Stewart platform, also known as the Stewart platform, is a six-degree-of-freedom parallel robot with a complex interaction structure. It is widely used in applications requiring high precision and fast response, such as vehicle simulation, automobile, ship, and boat ,as well as aerospace and medical. As a result, dealing with load changes during operation might lead the system to lose its high accuracy when using dynamic-based control models. To address this issue, this work begins with employing sliding mode control, a commonly utilized method for controlling high-order nonlinear systems. Then, for estimating the moving platform’s mass and moment of inertia, an adaptive load management strategy for the system is introduced. To objectively demonstrate the proposed control structure’s effectiveness, simulations are performed on the quasi-physical, Simscape simulation platform, provided by MATLAB. The simulation results demonstrate the outweigh performance of the proposed adaptive structure.
Robust adaptive control for a class of uncertain affine nonlinear systems
This paper presents the control synthesis method for a class of uncertain affine nonlinear systems based on adaptive and robust control techniques. The result is an identification law for the uncertain components, and a robust controller is constructed based on sliding mode control. The research results are simulated in Matlab Simulink with specific examples to demonstrate the correctness of the method. Moreoer, the control system proposed in this paper operates stably and achieves high control quality under uncertain conditions of plants. These research results can be applied to the design and development of control systems for appropriate control targets in practical applications.
Study on wind energy forecasting of Quang Tri region using artificial neural networks NARX
Quang Tri Province has a rich source of wind energy, but effectively harnessing this valuable resource requires consideration of several factors. One of these is wind forecast databases, which are crucial to optimizing wind energy utilization. The purpose of this study is to develop a wind speed forecasting model based on artificial neural networks using the NARX structure. Our model was constructed using historical data on wind speed and meteorological factors in Quang Tri. A local monitoring station's wind speed is used as an input variable for the NARX model. The MAPE and MAE metrics indicate that the forecasting model is highly effective, even under changing weather conditions. The proposed model has also been compared with models that use additional correlated input data, including wind direction, temperature, pressure and humidity, at various altitudes of 40m, 60m, 80m. The study also shows that choosing appropriate NARX model parameters can potentially expand the early forecast range up to 7 days. As a result of this validation, not only is the accuracy of the model determined, but also how the model can be used in order to harness wind energy efficiently and sustainably. Local wind energy management systems can benefit from this forecasting model by improving resource utilization and minimizing risks.
Optimization of sliding mode controller parameters for planar parallel robot
In this paper, we present a method for optimizing the parameters of a Sliding Mode Controller (SMC) using the Particle Swarm Optimization (PSO) algorithm. The SMC is well-known for its robust performance in handling system uncertainties and external disturbances. However, selecting the optimal parameters for the SMC plays a crucial role in achieving high performance in trajectory tracking tasks. Manual tuning often leads to suboptimal solutions, especially for complex nonlinear systems. To address this issue, we apply the PSO algorithm, a powerful parameter optimization technique inspired by the swarm behavior of birds or fish, to automatically search for the optimal control parameters. The optimized SMC parameters are then applied to the trajectory tracking control of a planar parallel robot. Simulation results, verified using Matlab Simulink, demonstrate high performance in trajectory tracking for the planar parallel robot.
An MIQP model for microgrid unit commitment Incorporating grid-connected/islanded operating modes and power network
This paper presents a mixed-integer quadratic programming (MIQP) model to address the day-ahead unit commitment (UC) problem for microgrids. The microgrid in this study includes conventional generators (CGs), renewable energy sources, and energy storage systems. The transition between grid-connected and islanded operating modes, as well as power grid constraints, is integrated into the proposed MIQP optimization model. The objective of the suggested formulation is to minimize the total operating costs of the microgrid, including the operating costs of conventional generating units and the expenses of purchasing electricity from the main grid. The MIQP model is derived from a mixed-integer nonlinear programming (MINLP) model by linearizing the product of two continuous variables and quadratic elements. The proposed MIQP model is implemented using the GAMS language with the commercial solver CPLEX and evaluated on a modified IEEE 33-node microgrid. The computational results demonstrate the effectiveness of the proposed optimization approach.
Empirical mode decomposition-based OS-ELM for short-term solar irradiance forecasting: A case study in Hanoi
Solar irradiation forecasting is vital for the growth of renewable energy sources. In this paper, we propose a hybrid model that integrates Empirical Mode Decomposition (EMD) and an online sequential extreme learning machine (OS-ELM) for multiple steps ahead forecasting of solar irradiation. Initially, the solar irradiation dataset is processed and cleaned. Then, using the EMD model combined with the autocorrelation function, the cleaned dataset is decomposed into several Intrinsic Mode Functions (IMFs) and white noise, which is removed. Each IMF is subsequently predicted using OS-ELM. The final solar irradiation forecast is derived by aggregating the predictions from all Intrinsic Mode Functions (IMFs). The model's performance was assessed through forecasting solar irradiation in Hanoi, using weather data from 2018. The data was collected at 1-hour intervals and utilized for single-step, 12-step, and 24-step ahead forecasts. The forecasting accuracy of the proposed model was compared with four other models, including both single and hybrid approaches: Bidirectional Long Short-Term Memory network, ELM, OS-ELM, and EMD-ELM. Two evaluation metrics of RMSE and MAE were used to assess the forecasting performance of the models. The computational results show that when the multi-step ahead increases, accuracy decreases. In any case, the proposed method outperforms the others, achieving the lowest error rates at 18,01 W/m2 for RMSE and 8,51 W/m2 for MAE at 24- step.
Multimode space-vector overmodulation technique for enhancing voltage transfer ratio in matrix converters
The control method of three-phase matrix converters (MCs) inherently limits the voltage transfer ratio (VTR) to 0.866 in the linear modulation region, which constrains the broader application of MCs. This paper presents a detailed investigation into a space-vector overmodulation technique aimed at enhancing the VTR of MCs. The proposed method integrates the principles of inscribed circle and hexagon vectors, as well as hexagon and basic vectors, to extend the modulation range. Explicit equations for calculating the duty ratios of active and zero vectors are derived to achieve improved VTR. The effectiveness of the approach is validated through simulation results, demonstrating accurate output voltage fundamental components.
Performance analysis of null-steering beamformers with phase-only and amplitude-only control via BAT algorithm
Recently, two types of BAT algorithm (BA)-based adaptive beamformers for pattern nulling in Uniformly Spaced Linear Arrays (ULA), utilizing either phase-only or amplitude-only control, have been proposed in previous studies. This paper evaluates the advantages and disadvantages of these approaches. Furthermore, to achieve higher performance, detailed recommendations for applying the optimized method in various scenarios are provided. To support this analysis, multiple ULA pattern scenarios with pre-set nulls were conducted. The simulation results are presented to validate the conclusions drawn.
Investigate model and reduction technique of common-mode noise for LLC resonant converter
Switch Mode Power Supplies (SMPS) are extensively employed in fields such as telecommunications, information technology, and electric vehicles, where energy efficiency is increasingly demanded. Modern SMPS designs prioritize compact size, high power density, and optimal efficiency to meet these requirements. The LLC resonant topology, commonly utilized in the DC/DC conversion stage with planar transformers, enables operation at high frequencies (hundreds of kHz) and facilitates soft switching across the full load range. However, a significant drawback of this topology is electromagnetic interference (EMI), particularly common-mode (CM) noise, which arises from high switching frequencies and parasitic capacitance in the components. This challenge is further exacerbated by the use of planar transformers. This study presents a CM noise model for the Half-Bridge LLC (HB-LLC) converter based on a two-capacitor transformer representation, aiming to analyze CM noise behavior and investigate practical, cost-effective methods for its reduction.
An improvement of the miniaturized microstrip patch antenna based on coupled microstrips for on-body sensors
This paper presents a low-cost miniaturized microstrip patch antenna (MPA) designed for on-body applications at a frequency of 2.45 GHz. The antenna is based on the coupled-microstrip principle using FR-4 material. The microstrip transmission line feeding method is used to easily integrate the antenna into the sensor chip on the same plane. The antenna has a gain of 2.28 dBi, a bandwidth of 2.62%, an efficiency of 46.5%, and overall dimensions of 48.6×48.6×1.6 mm³. To evaluate the performance of the proposed antenna, it is compared with three different antenna versions, considering the impact of the human body on the antenna and the specific absorption rate (SAR) of the human body. The proposed antenna shows excellent compliance with the requirements for on-body sensor antennas.