Wind energy is one of the green energy sources that can decrease reliance on fossil fuels such as oil and coal. Wind turbines harness the kinetic energy of the wind and transform it into mechanical energy, which is subsequently converted into electrical energy using a DC generator. The generated DC voltage is transformed into AC voltage by an inverter unit. However, conventional inverters generally produce square-wave outputs with high total harmonic distortion (THD), whereas AC power systems ideally require sinusoidal waveforms with low harmonic content. This research proposes the design of a renewable energy–based power supply system capable of generating both DC and AC voltages for low-power applications. The novelty of the proposed system lies in the use of a five-level inverter with fewer switching devices than the cascaded inverter topology, combined with a T-model inductive-capasitive-inductive (LCL) filter to suppress harmonic distortion at the inverter output. The method involves modeling and simulating the system design using MATLAB Simulink. Simulation results show that the five-level inverter produces an output voltage amplitude of 270 V at 50 Hz with a THD of 28.48 %. After applying the LCL filter (L1 = L2 = 0.01 H and C = 1 mF), the THD decreases to 2.56 % for a 10 Ω resistive load, 2.61 % for an RL load (100 Ω, 0.8 mH), and 2.54 % for an RC load (10 Ω, 1 µF). These values comply with the IEEE 519 standard requiring THD below 5 %. Therefore, the proposed system has potential for small-scale renewable energy applications.
Photovoltaic efficiency is frequently compromised by physical obstructions, resulting in partial shading conditions. This non-uniform irradiance condition severely distorts system characteristics by inducing multiple power peaks. This study proposes a novel fractional tent map-chaotic horse herd optimization (FTM-CHHO) algorithm for global maximum power point (GMPP). By integrating fractional-order memory and chaotic maps, FTM-CHHO enhances global search capabilities and prevents entrapment in local maxima. The method was rigorously validated through simulations and hardware experiments using a SEPIC converter. Simulations demonstrated that FTM-CHHO achieved 99.52 % to 100 % tracking accuracy with rapid convergence times of 0.32 to 0.62 s. Furthermore, hardware tests under real-world shading confirmed its robustness, maintaining 95.54 % to 98.26 % accuracy and converging within 10.1 s. FTM-CHHO significantly outperformed perturb and observe (P8O) and standard horse herd optimization (HHO). These findings confirm that FTM-CHHO provides a highly reliable, fast, and efficient solution for maximizing solar energy extraction under complex environmental variability.
Generally, multilevel inverters function as voltage step-down or step-up converters. This paper discussed a new type of multilevel inverter, specifically five-level and seven-level designs that use six active switches, which can increase or decrease the output voltage. The first study on using equal DC sources (E + E), known as symmetric, in the five-level setup for adjusting voltage was conducted, and then it was expanded to seven levels using an uneven DC source (2E + E), called asymmetric, with a new control method that works differently for each function. This multilevel voltage step-up (boost) and step-down (buck) inverter fundamentally has two components: a DC voltage level generator and a voltage step-up and polarity generator. This work provides a detailed look at how the multilevel voltage boost and buck inverter is built to show the features of the suggested design. Simulation data is provided to confirm how well each proposed design works with five-level and seven-level voltage increases and decreases in an inverter, using the sinusoidal pulse-width modulation (PWM) index and PWM duty cycle for better understanding. By the simulation, the total harmonic distortion (THD) value of the inverter when it operates as a buck is 1.26-1.32 %, and when it operates as a boost, it is 1.32-1.36 %. These values are still below 5 % in accordance with IEEE 519 standards and demonstrate a unity power factor.
This study develops a generalized displacer motion equation for a beta-type Stirling engine. The proposed equation approximates the ideal Stirling cycle while maintaining sinusoidal piston motion to ensure stable power extraction. The displacer trajectory is modeled with a Fourier series and optimized. The resulting trajectory is then generalized as a piecewise function to improve applicability across different geometries. This approach improves control of working fluid distribution, allowing the expansion and compression processes to more closely approach isothermal conditions. The results show that the optimized Fourier trajectory achieves 91.9 % of the ideal Stirling-cycle work output, outperforming conventional drive mechanisms, where it only achieves 59.8 % for crank mechanism, 66 % for Scotch yoke, and 68.5 % for rhombic drive. For practical implementation, the optimized Fourier trajectory is generalized using a piecewise formulation. The generalized trajectory maintains approximately 80–90 % of the ideal Stirling-cycle work over a range of compression ratios without requiring re-optimization. These results demonstrate that the proposed approach provides both high thermodynamic performance and improved adaptability compared with conventional Stirling engine drive mechanisms.
This work presents an efficient motion planning framework for four-wheel steering (4WS) autonomous vehicles operating in complex and unknown environments. To improve planning efficiency, the framework employs a lazy edge-based A* (LEA*) algorithm for global path planning, adaptive fourth-order Runge–Kutta model predictive control (RK4-MPC) for trajectory tracking and motion execution, and wheel force distribution control (WFDC) to ensure stable motion during steering maneuvers. Quantitative results show that the LEA* reduces planning time by 87.5 % edge evaluations by 96.1 % compared to conventional A*, while improving path smoothness by 51 %. The integration of adaptive RK4-MPC with WFDC achieves the lowest tracking error and heading error of 34.8 % and 37.5 % compared to OMNI, and 28.6 % compared to S-4WS. In addition, the proposed method reduces the wheel slip ratio 88.4 % better than OMNI and 46.7 % better than S-4WS, while also reducing yaw acceleration by 50 % compared to both baselines. For computational efficiency, the proposed framework achieves a search time of 0.5234 s, 83.1 % faster than OMNI, and 37.1 % faster than S-4WS, and an optimization time of 1.4892 s, 30.3 % faster than S-4WS. Overall, the proposed framework improves motion planning efficiency while maintaining smooth and stable motion in simulation.
Traditional automated guided vehicles (AGVs) are restricted by their reliance on predefined paths, limiting adaptability in dynamic warehouse environments. While autonomous mobile robots (AMRs) overcome this limitation through on-board simultaneous localization and mapping (SLAM) and autonomous navigation, standard configurations often suffer from top mounted-sensor blind spots when loads are carried on the chassis. To address these coverage gaps, an indoor logistic AMR based on the robot operating system 2 (ROS2) was designed and evaluated. The platform was developed by combining a multi-LiDAR perception stack with low-cost industrial actuation and a lightweight, fleet-style user interface. Within the system architecture, data from two light detection and ranging (LiDAR) sensors were merged at the topic level into a single virtual scan for SLAM toolbox and Nav2. Additionally, actuation and wheel odometry were driven by an RS-485 Modbus-based brushless DC (BLDC) motor controller, while ultrasonic sensors for short-range safety, an inertial measurement unit (IMU) for orientation, and a network of microcontroller calling stations communicating via message queuing telemetry transport (MQTT) were integrated into the platform. Experimental validation demonstrated successful multi-LiDAR fusion, with the Modbus motor driver achieving a motion-control error of 0.36 % and a speed-retrieval error of 0.43 %. Furthermore, calling-station commands were reliably executed over MQTT, and a point-to-point navigational repeatability of 10.3 cm was achieved. These findings indicate that an integrated multi-LiDAR ROS2 AMR provides a highly practical solution for indoor logistics. Through the proposed sensor merger and calling-station handshake, two recurring vulnerabilities of standard ROS2 deployments—single-LiDAR coverage gaps and Nav2 goal-overwriting behavior—were successfully resolved.
Voice command recognition plays a crucial role in enabling intuitive interaction in robotic and embedded control systems. This study proposes a voice command classification system based on Mel-frequency cepstral coefficients (MFCC) and support vector machine (SVM) using the Google speech commands dataset v2. Eight command classes (“down”, “go”, “left”, “no”, “right”, “stop”, “up”, and “yes”) were used. The dataset was divided into 80 % training and 20 % testing sets, with hyperparameter tuning performed using 5-fold cross-validation on the training data. MFCC feature extraction employed 13 static coefficients augmented with delta and delta-delta features, resulting in a 39-dimensional frame-level representation and a 78-dimensional utterance-level feature vector. Experimental results show that the SVM with radial basis function (RBF) kernel achieved optimal performance with parameters C = 100 and γ = 0.01, yielding 96.2 % accuracy, 96.5 % precision, 96.0 % recall, and 96.2 % F1 score. The inclusion of dynamic features improved accuracy by 4.7 % compared to static MFCCs. The system demonstrates a lightweight architecture suitable for low-resource environments; however, experiments were primarily conducted under clean conditions, and robustness evaluation was limited to a single noise level (20 dB SNR). Furthermore, real-time deployment on embedded hardware was not experimentally validated and remains part of future work.
Robotics is transforming healthcare rehabilitation by improving precision, efficiency, and accessibility. While industrial robots are widely used, service robots in medical applications remain underutilized. Wrist rehabilitation is essential for restoring mobility and strength after injury or surgery. This study presents a three degrees of freedom (DOF) robotic system designed to assist patients with wrist mobility impairments, aiming to enhance rehabilitation outcomes. This study develops a 3-DOF wrist rehabilitation robot with active force control – proportional-integral-derivative (AFC-PID) control to enhance rehabilitation outcomes. Traditional rehabilitation methods for wrist impairments are time-consuming, physically demanding, and inconsistent. Patients require structured therapy to reduce stiffness, receive corrective assistance for incomplete movements, and strengthen muscles. The limitations of conventional treatments create a need for more efficient and accessible therapeutic solutions. The robotic system operates in three phases: phase 1 (passive mode) where the robot aids wrist movement to relax muscles, phase 2 (assisted mode) where the robot provides compensatory force for incomplete movements, and phase 3 (strengthening mode) where the robot applies resistance to build muscle strength. The robot operates in passive, assistive, and resistive modes. Force-sensitive resistor (FSR) sensors measure interaction forces, and control is implemented via ATMEGA 32 microcontroller. Simulation and experimental trials with male and female participants were conducted. A microcontroller regulates the torque, force, angular acceleration, angular velocity and angular magnitude and direction using a PID control strategy, which is applied in accordance with therapeutic protocols. Experimental results demonstrate that the system effectively reduces stiffness, assists movement completion, and strengthens muscles through controlled resistance. The robot achieves maximum torque of 1.00 N·m, angular velocity 0.55 rad/s, and high repeatability (intraclass correlation coefficient (ICC) b 0.98 . Mode C shows active engagement with performance approaching reference values. Automating rehabilitation improves treatment efficiency and accessibility, offering a promising solution for patient recovery. The system provides adaptive, reliable, and gender-independent wrist rehabilitation, demonstrating clinical potential. The developed 3-DOF wrist rehabilitation robot with AFC-PID control achieves a maximum torque of 1.00 N·m, angular velocity of 0.55 rad/s, and reduces perceived joint stiffness by up to 15 %, demonstrating precise, adaptive assistance across passive, assistive, and resistive modes.
This paper presents an advanced control strategy aimed at accelerating the temperature recovery time in industrial heating ovens, particularly in response to observable disturbances such as periodic door openings. The proposed method combines a Takagi-Sugeno-Kang (TSK) elliptic fuzzy-based adaptive proportional-integral-derivative (PID) control with a static feedforward (FF) control strategy. The TSK elliptic fuzzy system models the input-output dynamics and adaptively adjusts the PID gains, allowing the controller to respond effectively to varying system conditions. The static feedforward control is designed to specifically counteract the measurable disturbances to shorten recovery time and improve stability. The strategy is validated through both simulation and experiment on a low-cost STM32 microcontroller. Compared with a conventional PID controller, it reduced the RMSE by 32.27 % and the ISE by 54.13 %, together with a recovery time shortened by approximately 72 s, reflecting the faster disturbance recovery achieved by the static feedforward action. Experimental results confirmed these findings, with a higher feedforward gain further reducing the temperature drop and accelerating recovery. The proposed technique offers a reliable and practical solution for practitioners in managing similar disturbance patterns in industrial settings.
Mechanical speed governors in small generator sets often provide only coarse frequency regulation, leading to steady-state error and poor transient recovery under load disturbances. To address this limitation, this study proposes a hybrid governor for an (liquefied petroleum gas) LPG-converted generator, in which the built-in mechanical governor is retained as the primary stabilizing layer, and a model-free iterative learning control (ILC) is added as an assistive electronic controller. The proposed method was validated experimentally under dynamic multi-step load disturbances and internal parameter shifts. In the dynamic load test, the proposed hybrid ILC achieved the lowest root mean square error (RMSE) of 0.9144 Hz, compared with 0.9581 Hz for the (proportional-integral) PI-controller benchmark and 1.5512 Hz for the mechanical governor. This corresponds to an RMSE improvement of 41.05 % relative to the mechanical governor and 4.56 % relative to the PI-controller benchmark. In terms of relative tracking accuracy, both electronic controllers substantially reduced the mean absolute percentage error (MAPE) relative to the mechanical governor, with the proposed hybrid ILC achieving the lowest value of 1.14 %, slightly lower than 1.15 % for the PI-controller and much lower than 2.04 % for the mechanical governor. Under internal parameter detuning, the proposed method maintained better regulation performance, with RMSE improvements reaching 79.68 % relative to the mechanical baseline. These results show that the proposed hybrid model-free ILC improves transient response, tracking accuracy, and robustness, while preserving the original mechanical governor as a practical baseline controller.
The advancement of steer-by-wire (SbW) technology in the modern automotive industry demands efficient and safe testing methods for complex control systems. Conventional validation on physical prototypes is often prohibitively expensive and highrisk, particularly in the initial development phases where control algorithms are still immature. To mitigate these challenges, hardware-in-the-loop (HIL) simulation provides a crucial intermediate step, enabling rapid, cost-effective, and safe iterative testing of control algorithms in a controlled environment. This research presents the design, implementation, and validation of a haptic feedback control system for an SbW application using a low-cost HIL platform. The developed architecture integrates a physical steering wheel plant with a real-time virtual model of the front wheels, controlled via an NI MyRIO and LabVIEW. The control system performance was analyzed by comparing proportional (P) and proportional-derivative (PD) controllers. The proportional controller was tuned using an empirical approach, while the proportional-derivative controller was designed analytically using the pole-zero cancellation method. The results demonstrated a clear trade-off with the proportional controller, which produced physical oscillations on the hardware. In contrast, the proportional-derivative controller successfully eliminated overshoot and damped all oscillations, which was physically validated as a stable and responsive haptic feedback. This research successfully demonstrates that the HIL platform can effectively validate and differentiate the physical performance of control architectures, confirming the superiority of the proportional-derivative controller for achieving a stable, high-fidelity haptic feedback system for SbW applications.
High penetration of intermittent renewable energy sources (RES) fundamentally alters the voltage stability profile of electrical networks, creating significant challenges for regulation and efficiency. This paper investigates the optimal deployment of static var compensators (SVCs) to mitigate these instabilities in a 37-bus test system. The impact of integrating 15 MW of renewable generation is analyzed, comparing a PV-only scenario to a PV-wind hybrid system. A key contribution of this study is the comparative analysis between a centralized compensation strategy (using a single high-capacity SVC) and a distributed strategy (using dual SVCs with an equivalent total rating). Simulation results demonstrate that the distributed dual-SVC configuration is significantly more effective than the single-unit approach. This configuration ensures superior voltage stability, maintaining the minimum bus voltage above the critical threshold (≥ 0.9504 p.u.), and achieves a substantial 19.4 % reduction in active power losses. These findings confirm that distributing reactive power support provides a more robust solution for enhancing grid reliability and energy efficiency in hybrid renewable systems.
Biogas is a promising renewable energy source, but its production and purification processes often lack real-time monitoring, leading to suboptimal yields and inconsistent gas quality. To address this gap, this study aimed to design a prototype for an integrated monitoring system based on the internet of things (IoT). The developed system facilitates the continuous observation of key parameters in both the anaerobic digestion and the subsequent purification stages. The prototype was constructed using an ESP32 microcontroller as the central processing unit, which collected data from a suite of sensors. These sensors measured critical process variables, including the digester's slurry temperature and pH, the volume of the produced gas in the gasholder, and the concentration of methane (CH₄) and hydrogen sulfide (H₂S) before and after the purification unit. Data were transmitted wirelessly via a Wi-Fi network to a cloud-based IoT platform, allowing for remote, real-time data visualization on a web dashboard. The results demonstrated that the prototype successfully captured and transmitted all parameter data with high reliability. The system provided a clear, real-time overview of the digester's operational stability and effectively quantified the increase in methane concentration and the reduction of impurities post-purification. Testing shows stable data transmission to Google Sheets and InfluxDB with minimal data loss. Delay times increase with distance in Google Sheets, from 3736.1 ms (2 m) to 3880.2 ms (8 m), while InfluxDB delay varies. RSSI values decrease with distance, with an accuracy range of 0.28 % to 5.11 %, peaking at 99.17 % accuracy at 6.05 meters.
This study addresses the need for efficient aerodynamic design in fixed-wing unmanned aerial vehicles (UAVs) for aerial mapping applications, where flight stability and cruising performance are critical. The research aims to optimize wing geometry parameters to achieve the desired cruising speed while minimizing drag and ensuring static stability. The methodology integrates conceptual and preliminary design approaches, followed by aerodynamic simulations using XFLR-5 with the vortex lattice methodology (VLM-2). Three design variables, winglet length, cant angle, and twist angle, are systematically varied, and the response surface method (RSM) is employed to model and optimize their effects on lift, drag, and airspeed. The optimization results indicate that the optimal configuration achieves a cruising speed of 16.8 m/s with improved lift characteristics (CL ≈ 0.48) and controlled drag (CD ≈ 0.022). Further analysis confirms that the optimized UAV satisfies longitudinal, lateral, and directional static stability criteria under various control surface deflections. In conclusion, the integration of RSM with aerodynamic simulation provides an effective and systematic framework for enhancing the UAV performance and stability, particularly in aerial mapping missions.
