Abstract This study details the design, development, and preliminary field validation of a low-cost tracked mobile robot intended for autonomous navigation within plantain crop rows. Plantain is a significant crop in numerous tropical regions; however, robotic solutions for operations beneath plantain canopies are limited, presenting a compelling opportunity to explore the application of mobile robots in agriculture. In this regard, low-cost under-canopy robots offer promising alternatives for facilitating frequent in-field supervision and data collection. The proposed platform integrates two 12V DC motors with encoders, an ESP32 microcontroller for low-level proportional-integral (PI) motor control, and a Raspberry Pi 5 running Robot Operating System 2 (ROS~2) for high-level control.
The perception system incorporates a YDLIDARX2 LiDAR scanner and a BNO055 inertial measurement unit (IMU) to estimate both the lateral distance to the crop row and heading orientation. These measurements were used by a fuzzy logic controller to generate differential angular velocity commands, maintaining a target distance of 1.0 m from the right-hand crop row. Field experiments conducted under a canopy and on uneven, muddy terrain demonstrated reliable motion and acceptable lateral error stabilities. The integration of IMU-based heading estimation with the fuzzy reactive controller enhanced path alignment and effectively managed the external disturbances. The results suggest that the combination of low-cost components with a reactive fuzzy control strategy facilitates reliable under-canopy navigation in plantain plantations and establishes a foundation for the future popularization of low-cost robots supporting agricultural tasks in plantain crops.

The accumulation of dust and pollutants on solar panels significantly reduces their energy conversion efficiency, resulting in performance degradation and increased maintenance costs. This paper presents an AIpowered solar panel cleaning scheduler that autonomously detects dust accumulation and efficiency drops, and dynamically schedules cleaning operations. The proposed system integrates a solar-powered mobile rover equipped with an ultrasonic sensor, motor driver, DC motor, and real-time clock (RTC) for intelligent monitoring and timebased operation. The rover traverses the panel surface using a motor-driven mechanism to perform automated cleaning whenever a predefined drop in efficiency or increase in dust level is detected. The AI module, trained on real-time efficiency and environmental data, predicts the optimal cleaning interval by analyzing sunlight intensity, dust density, and power output trends. A microcontroller-based control unit coordinates sensor inputs, rover movement, and cleaning commands, while the 10V solar battery provides sustainable energy for system operation. Experimental validation demonstrates that the proposed design effectively maintains panel efficiency and minimizes unnecessary cleaning cycles, leading to improved energy yield and reduced water and labor usage. This work highlights the potential of combining AI, IoT sensing, and robotic automation for the development of self-sustaining, intelligent solar maintenance systems.

Accurate analysis of commutation phenomena in permanent magnet DC (PMDC) motors requires simultaneous consideration of electromagnetic field distribution and armature circuit dynamics. Classical circuit-based models are unable to properly capture transient effects occurring in short-circuited coils during commutation, while purely field-based models neglect the influence of the supply circuit. In this paper, a coupled field–circuit model of a PMDC motor with an innovative magnetic circuit based on rectangular NdFeB permanent magnets is presented. The model combines a two-dimensional finite element electromagnetic analysis with a segmented armature circuit and dynamic commutator switching, allowing the electromotive force to be computed individually for each coil based on the actual magnetic field distribution. The novelty of the proposed approach lies in the integration of a non-standard rectangular permanent magnet topology with a coil-resolved field–circuit commutation model, validated on a physical motor prototype. Simulation results are compared with experimental measurements obtained from a laboratory prototype at rotational speeds of 850 and 1000 r/min. The predicted electromagnetic torque shows good agreement with measurements, with deviations below 5%, while the armature current is estimated with an error of up to approximately 20%, primarily due to model simplifications. The developed model provides direct access to transient commutation waveforms and constitutes a practical tool for the analysis and design optimization of PMDC motors operating under dynamic conditions, particularly in cost-sensitive and reliability-oriented applications.

Brushless DC (BLDC) motors are becoming increasingly popular and are replacing brush motors in a variety of applications due to their improved mechanical and electrical features and simple design. This study proposes a unique hybrid FOPID controller optimized for Salp swarm FireFly (SSFFO) to address the controller tuning problem. This hybrid SSFFO-FOPID technique is intended to optimize speed controller performance under diverse operating conditions. Three-degree parameters, including torque, current and speed error values, can be optimized using an SSFFO to reduce both transient and steady-state responses. According to this study, a speed controller with Pulse Width Modulation (PWM) mode is utilized to manage torque. The torque and speed of BLDC motors can be adjusted with the FOPID. The proposed controller design in MATLAB 2022a for the optimal BLDC motor speed control was developed using Simulink models. To validate the suggested controller, a simulation is conducted with the BLDC motor at several predetermined speeds. The proposed strategy has a settling time that is 58.15%, 61.23%, 77.43% and 82.05% shorter than that of LHHO, HGWO, WOA and CVOA, respectively.

The main aim of this research is to design and develop an automatic wall painting robot for painting multiple colours. In the designed robot, an automatic painting mechanism has been made for painting two different colours by the use of an air compressor and spray guns. Also, a vertical and horizontal systems have been made for the painting mounting of the spray guns. The performance of the developed robot was tested by five different testing which were DC motor testing, Stepper motor testing, Paint testing, Liquid level sensor testing, and efficiency testing. The designed project has the ability to paint an area of 0.270 m2 in 26.760 minutes which has an accuracy of 96.476%. The efficiency of the painting system has an efficiency of 96.296% in terms of the painted area. The painting efficiency increased by decreasing the separation distance between the wall and the robot and fixing a pressure to be 4.5 bar. The overall efficiency of the entire system has been obtained which was 83.987%. The system has a strong mobility system for reducing the vibration generated by the stepper motors. There were three CNC linear lead screw 500 mm each fixed on the mobility system of the hardware. Two lead screws were fixed vertically and one lead screw was fixed into the two vertical sliders horizontally. The system uses a rocker switch 3-way to either select one of the two colors to be applied into the wall or leave the slider to continually move till it reaches a specific area to select the color to paint that special area.

Water hyacinth (Eichhornia crassipes) is among the world's most aggressive invasive aquatic weeds. Its rapid proliferation forms thick floating mats that block sunlight, deplete dissolved oxygen, impede navigation, degrade water quality, and severely threaten aquatic biodiversity and livelihoods, particularly in tropical and subtropical regions. We present a low-cost, fully autonomous catamaran system designed for targeted detection and mechanical removal of floating water hyacinth in small-to-medium water bodies. The perception pipeline combines two deep-learning models deployed on an NVIDIA Jetson Nano. First, a UNet architecture performs pixel-level segmentation of aquatic vegetation from real-time RGB images (mean Dice coefficient 0.906 ± 0.04, mean IoU 0.831 ± 0.06, evaluated on a custom dataset of 7282 real-world image-mask pairs collected from moving platforms under varied lighting and water conditions). The resulting mask is then fed to a fine-tuned VGG19 classifier that discriminates water hyacinth from other vegetation and floating debris with 96% accuracy, precision 0.97, recall 0.95, and F1-score 0.96. Detection results are mapped to four image quadrants, triggering simple yet robust rudder commands that reliably centre patches in the field of view. The 75cm twin-hull vessel, 3D-printed from Polyethylene Terephthalate Glycol (PETG), is propelled by twin brushless DC motors and carries a passive rear conveyor-belt collector that scoops and stores up to 25kg of wet biomass. Field trials conducted in natural ponds and canals in Chennai, India, confirmed stable real-time performance, effective quadrant-based navigation, and successful autonomous collection across diverse weather conditions. This affordable, open-design solution offers immediate deployability and straightforward scalability through fleet operation, directly supporting UN Sustainable Development Goals 6 (Clean Water and Sanitation) and 9 (Industry, Innovation and Infrastructure).

The aim of the project is to design and construct a predictive maintenance system on an elevator system using machine learning. Three objectives are set for the project for the system to be archived. The first objective is to develop the machine learning technique to monitor the health of the elevator system. The elevator system chosen for the predictive maintenance system was permanent magnet synchronous motor traction elevator. The PMSM data set was proceeded for the data analysis. Smoothening of the data were completed as there were too many peak data in the data set. Using the smoothen data set, threshold was created for the classification of the output (health condition) by comparing with the “time” parameter data. Once the output has been classified and tabulated into the data set, the completed information data set will be transferred into the model for training purpose. The second objective was to design a prototype framework of an elevator for data collection. Arduino Uno was chosen as the microcontroller for the elevator prototype. DC motor was selected to representing the elevator motor that drives the elevator car. Two sensors: LM35 and Encoder Sensor Module were selected for the data capturing objective. LM35 is capturing the temperature data ad Encoder Sensor Module will capturing the rotation per minute data form the DC motor. The data collected will be compiled into a file before transferring it to the MATLAB processing. The last objective is to evaluate the performance of efficiency of the system. Total of 5 testing were conducted for the implemented system. The first three was about the setting of training model, the result was Fine KNN algorithm has the most accuracy of 93.8%. The fourth testing was conducted on checking the prediction ability of the trained model. The analysis shows the trained model maintained its accuracy even when extending the range of time for prediction. The fifth testing is about unbiased prediction of the trained model. The final result of the unbiased prediction accuracy was 95.5%.

Design of an accessible turbulence chamber for laboratory experiments

The integration of power electronic converters in automotive systems has led to increased challenges in managing Electromagnetic Interference (EMI), particularly in components such as fuel pump drive systems. This paper presents an experimental characterization of conducted EMI-both common-mode (CM) and differential-mode (DM), generated by a synchronous DC-DC converter used to supply a 12V DC motor in a vehicle fuel pump. Two types of power switches, MOSFET and IGBT, are analyzed under identical operating conditions to evaluate their influence on EMI performance. The study is conducted using a dedicated experimental test bench that allows for accurate measurement of EMI emissions in real-world conditions. Results reveal distinct EMI profiles depending on the switching device employed, with the MOSFET configuration showing higher DM noise due to faster switching dynamics, while the IGBT offers a more moderate EMI spectrum. This experimental investigation highlights the importance of switch selection in automotive power electronics and contributes to the design of more EMC-compliant and reliable converter systems for fuel pump applications and beyond.

The aim of the project is to design an obstacle avoidance system by using neural network. The main functionality of the project is to predict the object detected and to find a path of avoidance the detected object. Due to the increase in autonomous vehicle centered around machine learning technology, expensive system which incorporates multiple sensors for the obstacle avoidance and object detection are made. To fill this gap, the project is designed to implement an object detection and obstacle avoidance system using a camera as the main component for detection. Jetson nano board is used as the main computer, NoirV2 camera is used as the main vision sensor, 3d printed structure for the body, two dc motor with a I2c based motor driver. SSD MobileNet v2 is used as the model for object detection, Jetson inference is the training guide which is used for the obstacle avoidance and object detection which is optimized to work with Jetson nano board providing a faster detection and fps. The project includes a web based Graphical user interface to control the robot and to monitor it. The project will solve the issues of expensive system and requirement of multiple sensors for object detection and obstacle avoidance.

As a highly integrated and compact power unit, the motor-pump finds critical applications in emerging electric vehicle (EV) domains such as electro-hydraulic braking and steering systems, where its vibration and noise performance directly impacts cabin comfort. A key factor limiting its NVH (Noise, Vibration, and Harshness) performance is the electromagnetic vibration and noise induced by the cogging torque of the built-in brushless DC motor (BLDCM). Traditional suppression methods that rely on stator auxiliary slots exhibit certain limitations. To address this issue, this paper proposes a collaborative optimization method integrating multi-parameter scanning and response surface methodology (RSM) for the design of auxiliary slots on the motor-pump’s stator teeth. The approach begins with a multi-parameter scanning phase to identify a promising region for global optimization. Subsequently, an accurate RSM-based prediction model is established to enable refined parameter tuning. Results demonstrate that the optimized stator structure achieves a 91.2% reduction in cogging torque amplitude for the motor-pump. Furthermore, this structure effectively suppresses radial electromagnetic force, leading to a 5.1% decrease in the overall sound pressure level. This work provides a valuable theoretical foundation and a systematic design methodology for cogging torque mitigation and low-noise design in motor-pumps.

Reinforcement learning offers efficient solutions for optimizing complex decision-making tasks through continuous state-action-reward cycle with real-time adaptability. This work presents twin delayed deep deterministic (TD3) policy gradient RL based adaptive speed controller for the DC motor model while considering the impact of various uncertainties from dynamic environment into account. Various benchmark controller techniques are also utilized for similar objective in order to perform comparative analysis. Responses of each controller are plotted for both constant and variable desired speeds to evaluate their efficacy, robustness, and adaptability to uncertainties. Values of various types of error indices, including integral of squared error (ISE), integral of time-weighted absolute error (ITAE), integral of absolute error (IAE), integral of time-weighted squared error (ITSE), and their respective time-weighted variants are calculated, and tabulated for each type of speed controller for both test cases. Error indices analysis is also utilized to compare, and evaluate each controller’s tracking precision and error minimization qualities in dynamic operating conditions for efficient speed regulation for the DC motor.

Abstract A novel method for 3D image registration of neutron/X-ray imaging for cylindrical lithium batteries is proposed for addressing inconsistencies in image size, grayscale distribution, and spatial alignment caused by geometric and parameter differences in heterogeneous imaging systems. Considering the complex internal structure and multi-material distribution of lithium batteries, a multi-stage registration process is designed, including verticality adjustment, slice height mapping, scale transformation, translation and rotation calibration. The process is enhanced by incorporating image super-resolution reconstruction techniques to improve image clarity and detail representation. Comparative and extended experiments are conducted using cylindrical lithium batteries and micro DC motors to evaluate the performance of the registration method in terms of spatial alignment accuracy, multi-modal information fusion, and detail fidelity, thereby validating the effectiveness of the method. Results indicate that the proposed approach effectively corrects geometric and modal differences between heterogeneous images, achieving precise alignment of metallic and non-metallic regions. Quantitative analysis based on metrics such as Dice Similarity Coefficient (DSC), Normalized Mutual Information (NMI), and Gradient Similarity (GS) demonstrates the significant advantages of the method in registration accuracy, edge preservation, and modal feature alignment. The method exhibits high adaptability and stability in characterizing the complex structures and multi-modal characteristics of cylindrical lithium batteries.

This study presents a comprehensive performance analysis of four different control strategies, Proportional–Integral (PI), classical Sliding Mode Control (SMC), Super-Twisting SMC (ST-SMC), and Exponential Reaching Law SMC (ERL-SMC), applied to the speed regulation of a Hall-effect sensored Brushless DC (BLDC) motor. A mathematically detailed BLDC motor model, three-phase inverter structure with safe commutation logic, and a high-frequency PWM switching scheme were implemented in the MATLAB/Simulink-2024a environment to provide a realistic simulation framework. The control strategies were evaluated under multiple test scenarios, including variations in supply voltage, mechanical load disturbances, reference speed transitions, and steady-state operation. The comparative results reveal that the classical SMC and PI controllers suffer from significant oscillations, overshoot, and limited disturbance rejection capability, especially during voltage and load transients. The ST-SMC algorithm improves robustness and reduces the chattering effect inherent to first-order SMC but still exhibits noticeable oscillations near the sliding surface. In contrast, the proposed ERL-SMC controller demonstrates superior performance across all scenarios, achieving the lowest steady-state ripple, the shortest settling time, and the most stable transition response while significantly mitigating chattering. These results indicate that ERL-SMC is the most effective and reliable control strategy among the evaluated methods for BLDC speed regulation, which requires high dynamic response and disturbance robustness. The findings of this study contribute to the advancement of SMC-based BLDC motor control, providing a solid foundation for future research that integrates observer-based schemes, adaptive tuning, or real-time hardware implementation.

Electrifying Indonesia’s motorcycle fleet is critical for reducing urban emissions and fossil fuel dependence. This study experimentally evaluates three powertrain configurations—hub motor, continuously variable transmission (CVT), and single-gear ratio—for converting internal combustion engine (ICE) motorcycles to electric two-wheelers (E2W). Using a Honda Vario 125 platform with a 72 V, 3 kW Brushless DC motor and 1.44 kWh lithium-ion battery, performance was assessed via chassis dynamometer and real-world urban road tests. The single-gear ratio configuration demonstrated superior overall performance, achieving 5.15 kW peak wheel power, 188.7 N·m torque, fastest acceleration (0–128 km/h in 22 s), and highest energy efficiency (37.0 km/kWh), enabling a 51.8 km range per charge. The hub motor excelled in top speed, while the CVT consistently underperformed. Benchmarking shows up to 104 % efficiency improvement over prior designs. These results provide quantitative guidance for converters, manufacturers, and policymakers, establishing the single-gear ratio as the optimal solution for urban and commercial E2W applications and supporting sustainable mobility initiatives.

The Motor Operated Valve (MOV) is a critical component in fluid control systems at Steam Power Plants (PLTU). Training new technicians is often hindered by limited access to actual equipment and operational safety risks. This research aims to design and develop an Arduino-based MOV control module simulator capable of simulating basic functions such as open, close, stop, and limit switch responses. The method used is Research and Development (R&D) with an experimental approach. The simulator was tested using a DC motor as the simulated valve actuator, equipped with push buttons, relays, limit switches, and indicator lamps for visual feedback. The test results showed that the simulator successfully represented control functions with 100% accuracy in limit switch responses and consistent operation. User evaluations involving ten new technicians indicated an 85% satisfaction rate in terms of ease of understanding and operational safety. This simulator has proven to be an effective, interactive, and safe learning medium for new technicians at PLTU Nagan Raya.

Introduction. This study proposes a finite-time robust control law for position tracking of a DC motor under conditions of model uncertainty and external disturbances. The motor operates through a pulse-width modulation (PWM) unit and an H-bridge power circuit, aiming to achieve finite-time position tracking while minimizing the effects of model uncertainties and external disturbances. Problem. The main challenge lies in achieving accurate and rapid position and speed regulation for the DC motor while maintaining high performance, despite model inaccuracies and external disturbances. The goal of this paper is to design a robust finite-time position tracking control law for a DC motor based on the differential geometric approach, ensuring high tracking accuracy and control efficiency in the presence of disturbances and parameter uncertainties. Scientific novelty. The integration of finite-time control based on a virtual system, diffeomorphism transformation, and disturbance compensation introduces an innovative solution for DC motor position tracking under incomplete modeling and external perturbations. Methodology. The study employs the differential geometric method to construct a virtual system with finite-time characteristics and uses Lyapunov theory to prove global stability in the presence of uncertainties and disturbances. A finite-time virtual system is proposed after analyzing the incomplete dynamic model of the DC motor. Results. To validate the proposed approach, MATLAB simulations were conducted and compared with a conventional sliding mode controller. The results demonstrate improved settling time and robustness of the proposed method in DC motor position tracking. The findings confirm that the proposed controller provides intuitive and precise control, accurate position tracking, and enhanced performance regulation. It also exhibits strong robustness against model uncertainties and external disturbances. The practical value of the proposed method is considerable, as it offers a reliable and efficient position control scheme for DC motors using PWM. The method ensures precise position control and robust performance under varying conditions and external interferences, making it well-suited for real-world DC motor control applications. References 23, tables 1, figures 12.

This study presents a LiDAR-based obstacle detection system for a small-scale, Bluetooth-controlled vehicle. The purpose of the system is to take precise distance measurements and to activate automatic detection and avoidance of obstacles. The integration comprises an ESP32 microcontroller, LiDAR sensor, motor driver, DC motors, LCD display, and buzzer, all governed by the Dabble mobile application. The vehicle keeps on measuring the distances and when an obstacle is within a 3 cm threshold, the forward movement is stopped and visual and auditory alerts are triggered. The testing done in a control indoor environment revealed that the system was able to detect obstacles, give precise distance readings and respond instantly with no delay all the time. The simultaneous alerts increased usability and safety. In general, the system is a dependable, user-friendly solution for small robotic vehicles and it has potential for wider applications in areas such as IoT and robotics.

WaveSpectro-XAF: Dual-branch deep learning with STFT spectrograms for automotive brushed DC motor noise classification

Abstract A control strategy is proposed for a trapezoidal back-EMF permanent magnet synchronous motor (commonly referred to as a brushless DC motor) driven by a four-switch, three-leg inverter. The motor is regulated using field-oriented control (FOC) with an outer speed loop governed by a Dung-Beetle Optimization (DBO) algorithm–based Artificial Neural Network tuned Proportional–Integral–Derivative (D-BAP) controller. The PID gains are optimized at discrete reference speeds ranging from 50 rpm to 3000 rpm under three load conditions (low, medium, and high) using the Integral of Time-weighted Absolute Error (ITAE) criterion. The optimized gain sets, along with the corresponding reference speed, actual speed, battery current, and battery voltage, are compiled into an extensive dataset used to train the ANN for adaptive online tuning. The trained network continuously updates the controller gains in real time, enabling robust adaptation to variations in mechanical load and DC-link voltage. The proposed D-BAP controller is benchmarked against conventional PI, fuzzy–PI, Model Predictive Control (MPC), and Sliding Mode Control (SMC), with both simulation and experimental results demonstrating superior speed tracking, minimal steady-state error, and enhanced robustness under nonlinear operating conditions.