PROJECTS/PROTOTYPES
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Advancing UAV Technology for Reliable Oil and Gas Pipeline Monitoring.
International MS Student's work from the Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malayisa
Abstract: This Project evaluates methods for extending UAV flight endurance, focusing on their potential application in pipeline inspection. Through an extensive literature review, this study identifies the latest advancements in UAV technology, evaluates their effectiveness, and highlights the existing gaps in achieving prolonged flight operations. Advanced techniques, including artificial intelligence (AI), machine learning (ML), and deep learning (DL), are reviewed for their roles in pipeline monitoring.
Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm.
International PhD Student's work from the Department of Electronic Engineering, IBA Sukkur University Sukkur Pakistan
Abstract: This project focuses on enhancing GNSS performance in challenging environments by addressing the critical issues of multipath (MP) and non-line-of-sight (NLOS) signal receptions. A novel Received Signal Quality Monitoring (RSQM) algorithm is developed, combining fuzzy logic-based signal classification with an adaptive navigation strategy. Using key signal parameters—CNR, NRR, and CCD—the RSQM algorithm classifies signal quality and dynamically excludes degraded measurements while preserving satellite geometry constraints. Evaluated across four GNSS constellations (GPS, GLONASS, BeiDou, Galileo), the system demonstrates a significant improvement in position accuracy from 5.4 m to 2.3 m in complex urban scenarios. The project advances the reliability of GNSS in support of Industry 5.0, enabling smarter and more sustainable autonomous navigation solutions.
A Synergistic Fractional-Order Control for Precise Helical Trajectory Tracking and Formation Stability in Multi-Agent Quadrotor UAVs.
Performed under the Internal Funded Project by IRC for Aviation and Space Exploration KFUPM
Abstract: This project presents a hybrid control strategy to enhance helical trajectory tracking accuracy and formation stability in quadrotor UAV (QUAV) swarms operating in real-world, dynamic environments. Recognizing the interdependence between trajectory precision and formation coherence, the proposed framework integrates a Fractional-Order PID (FOPID) controller to improve transient response, reduce overshoot, and ensure robust performance under disturbances. In parallel, a decentralized nonlinear formation control approach—based on consensus algorithms and vector field path following—is employed to maintain flexible and circular formations without requiring fixed leader-follower structures. Theoretical stability is rigorously ensured through Lyapunov’s direct method and the Grönwall–Bellman lemma, guaranteeing global asymptotic stability of the system. Simulation and real-world experiments validate the hybrid approach, demonstrating effective management of both tracking and formation challenges. This research contributes to advancing multi-agent UAV systems in applications such as aerial surveillance, environmental monitoring, and search and rescue missions, by offering a scalable and robust control framework suitable for complex and dynamic scenarios.
PLC-Enabled Solar Water Pumping System with MPPT and Boost Converter Integration for Energy-Efficient Irrigation
International MS Student's work from the Department of Electrical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China
Abstract: This study examines solar water pumping systems with programmable logic controllers (PLCs) to fill a gap in the literature. PLC-based automation can improve these systems' reliability and efficiency. However, PLC integration with sensors, actuators, and pumps, power consumption optimization, maintenance, and cost-effectiveness prevent their broad implementation. This work analyses two configurations: a DC-DC boost converter-based MPPT solar water pump control scheme and a thorough PLC-based water pumping component research. A well-designed DC-DC boost converter provides motor voltage. This MATLAB-supported method uses solar panels and pumps to solve water scarcity and conserve energy in developing nations.
Modified integrated track splitting (MITS)-Based Multi-Target Tracking in Cluttered Environments.
Jointly Performed with the researcher from Defense Systems Engineering Department Sejong University, Seoul, Republic of Korea
Abstract: Our team developed a Modified Integrated Track Splitting (MITS) algorithm to improve multi-target tracking in cluttered environments with occlusion. By leveraging adaptive data association and dynamic gating thresholds, MITS significantly reduces false detections and computational load. Simulations show a 91% reduction in complexity and 25% improvement in false track discrimination over traditional ITS and JITS methods, especially in challenging cross-over and occlusion scenarios.
Modeling and Control of Thrust Vectoring Rocket Systems Using PID and FOPIDs.
Project completed by the undergraduate students of Aerospace Engineering Department of KFUPM KSA
Abstract: This project focuses on the design, modeling, and control of thrust vectoring rocket systems, offering a practical learning resource for students and early-career researchers. A dynamic model is developed considering real-world effects such as nozzle oscillations, thrust variations, and changing inertia. Two control strategies—PID and Fractional-Order PID (FOPID)—are implemented and compared, with FOPID demonstrating improved stability, adaptability, and performance. The project also includes prototype development using SolidWorks and experimental validation, bridging theory and hands-on application in aerospace control systems.
Click here to read our Journal Publication and Click here for Conference Paper
Real-Time Detection of Anomalous UAVs in Swarms Using Graph-Based Signal Strength Modeling
This Project has been funded by IRC for Aviation and Space Exploration KFUPM under cost centre INAE-2408. The budget approved for this project was SAR 45000.
Abstract: This project presents a novel approach for detecting anomalous drones within UAV swarms using Graph Attention Networks (GAT) and RSSI data. By analyzing signal strength deviations and modeling drone communication through a radius graph, the system identifies irregular patterns indicating potential threats. A V-cycle GAT framework with graph coarsening and refinement enables accurate detection in real-time. The approach was validated through simulations and real-world testing using the Robolink CoDrone kit, demonstrating superior accuracy and efficiency compared to traditional methods. This work contributes to enhancing UAV network security through intelligent, signal-based anomaly detection.
Data Driven Traffic Density Monitoring: A UAV and ResNet50 Deep Learning Framework
Joint Research work with the researchers from Department of Computer Engineering, Sharjah University UAE and School of Computer Science Southeast University Nanjing China
Abstract: This project explores a UAV-based solution for urban traffic density classification using deep learning techniques. A modified ResNet50 model was trained on a custom UAV-captured dataset from Dammam, Saudi Arabia, incorporating diverse traffic scenarios with advanced normalization and augmentation. The optimized model achieved high accuracy, demonstrating strong adaptability for real-time traffic monitoring. This work highlights the potential of combining aerial surveillance and AI to enhance traffic analysis and support smarter urban planning and congestion management.
Input Delay Compensation via Barrier Lyapunov and Fuzzy Padé Approximation for Precise Trajectory Tracking for Quadrotor UAVs
Project performed by undergraduate student of AE Department KFUPM under Guided Research Fund
Abstract: This project presents a robust control framework for quadrotor UAVs that explicitly addresses the often-overlooked issue of input delay, which can significantly affect real-world flight performance. By integrating a Barrier Lyapunov Function (BLF) to maintain tracking errors within defined limits and applying a Fuzzy Padé approximation with an intermediate variable, the approach ensures stable and accurate trajectory tracking. Simulation results confirm the method’s effectiveness, making it well-suited for mission-critical and sustainable UAV operations.
Smooth Transition MMPC for High-Frequency Attitude Control in Quadrotor UAVs
Project performed by undergraduate student of Control and Instrumentation Department KFUPM under Guided Research Fund
Abstract: This project proposes a Multi-Model Predictive Control (MMPC) framework for quadrotor attitude regulation, offering the high performance of Nonlinear MPC (NMPC) with the computational efficiency of Linear MPC (LMPC). The framework utilizes multiple linear models with a smooth adaptive gain scheduling mechanism to avoid the chattering effects commonly seen in traditional MMPC, which can lead to motor noise and actuator wear. Simulation results demonstrate that the proposed MMPC approach outperforms conventional methods—including NMPC, LMPC, INDI, and SMC—by delivering stable, precise, and computationally efficient control for real-time UAV applications.
Click here to read our research paper
(Awarded with Best Paper Award click here to see the news)
Quadrotor Precision Landing on Inclined Surfaces Using Adaptive Leg Design with Robust PID and Fractional PID Control
Project performed by undergraduate student of AE Department KFUPM under Guided Research Fund
Abstract: This Project introduces a new system with adaptive landing legs to improve the versatility of quadrotor aerial platforms, facilitating safe take-off and landing on sloping surfaces. The design has a lightweight, symmetric leg construction powered by a single servomotor, reducing weight and streamlining wiring, while ensuring stability and balance in flight. To achieve accurate landings on slopes, essential data—including distance, slope orientation, and surface angle—is collected by a multi-zone Time-of-Flight sensor, which demonstrates resilience to visual distortions and fluctuating lighting conditions, surpassing conventional imaging techniques. Landing manoeuvre control is refined using MATLAB simulations, whereby we implement and compare proportional-integral-derivative (PID) and fractional-order PID controllers, emphasising the superior stability and precision of our control methodology. This design markedly enhances the manoeuvrability of quadrotors in difficult terrains.
Design and Analysis of the Effect of Trimmable Vertical Stabilizers for Enhanced Aircraft Maneuverability and Directional Stability
Project performed postgraduate students of AE Department KFUPM Saudi Arabia
Abstract: The vertical stabilizer, a critical component of an aircraft, ensures stability during flight by correcting yawing motion. This project focuses on the design and analysis of a trimmable vertical stabilizer for enhanced maneuverability and directional stability, has practical implications for the field. Using computational fluid dynamics (CFD) and a state-space model, the study evaluates the stabilizer’s impact on control moments, angle of turns, and overall flight efficiency. The key results demonstrate improved aerodynamic performance with significant gains in maneuverability and stability. This project concludes that incorporating a trimmable vertical stabilizer can lead to improved flight performance, particularly in challenging conditions such as crosswinds, thereby informing future aircraft design and operation.
Integrating AI-Driven Robust Control Algorithm with 3D Hand Gesture Recognition to track an Underactuated Quadrotor Unmanned Aerial Vehicle (QUAV).
Offered by Deanship of Student Affairs KFUPM
Abstract: This research focuses on the integration of an AI-driven robust control algorithm with 3D hand gesture recognition to track an underactuated Quadrotor Unmanned Aerial Vehicle (QUAV). The system utilizes 3D hand gestures for intuitive control, allowing users to perform complex, real-time operations with ease. Battery-operated and capable of aggressive maneuvers, this QUAV demonstrates promising advancements in autonomous flight technology. By combining machine learning with gesture-based interaction, the design enhances both control precision and responsiveness, making it suitable for tasks requiring high agility and autonomy in diverse environments. (Click here to read research paper)
Underactuated Quadrotor Unmanned Aerial Vehicle (QUAV)
Abstract: This research presents the development and prototyping of an Underactuated Quadrotor Unmanned Aerial Vehicle (QUAV) designed to test a control strategy combining a single-dimension Fuzzy Sliding Mode Controller (SMC) with a Linear Extended Observer and Disturbance Observer. The QUAV features data logging via an SD card for performance analysis and is remotely operated using a 6-channel RC control system. Battery-powered and equipped with a robotic manipulator, it can lift payloads up to 500 grams. The control architecture is designed to enhance stability and robustness, particularly under challenging conditions like external disturbances and unmodeled dynamics. The integration of fuzzy logic with SMC offers smooth, adaptive control, while the disturbance observer and linear extended observer improve real-time estimation and compensation for system uncertainties, making it an ideal platform for advanced UAV research and applications.
Fault Control Design for a Small Scale Quadrotor UAV
Abstract: This project focuses on the fault control design of a small-scale quadrotor UAV, which has been enhanced for improved performance and stability. The UAV features data logging through an SD card, allowing for real-time performance monitoring and analysis. It is remotely operated using the Tello Drone Application, providing ease of control. The design includes mechanisms to reduce chattering noise during operation, enhancing the smoothness of the flight experience. The quadrotor is equipped with a mini-solenoid gripper capable of lifting and dropping payloads of up to 50 grams, making it suitable for light object transportation tasks. This project demonstrates advancements in noise reduction and payload handling for small-scale UAV applications.
Underactuated Air Cushion Vehicle (ACV) on Carbon Fibre Structure
Abstract: This project involves the design and development of an Underactuated Air Cushion Vehicle (ACV) constructed on a lightweight carbon fiber structure. The ACV is remotely operated using a 6-channel RC control system and is battery-powered for mobility. It features data logging capabilities via an SD card, enabling real-time monitoring and analysis. The ACV is equipped to lift payloads of up to 50 grams, making it suitable for light transport applications. Additionally, it shares operational parameters using an IoT broker, integrating with smart systems for enhanced control and data exchange. This project demonstrates innovation in lightweight, remotely operated vehicles with IoT capabilities.
Unmanned Under-water Vehicle (UUV) Prototype
Abstract: This project aims to create a prototype of an Unmanned Underwater Vehicle (UUV) intended for marine exploration and surveillance. The UUV possesses functionalities for data and video logging, which are recorded on an SD card for subsequent analysis. It functions using a wired control system, guaranteeing reliable communication in underwater settings. The UUV employs IoT technology to transmit real-time operational parameters to an IoT broker, facilitating improved monitoring and remote control. A significant attribute of this prototype is its capacity to recognise aquatic organisms, rendering it an essential instrument for undersea research and studies of marine biodiversity.
Sewerbot Design with Rudder Design
Abstract: This project outlines the design of a sewerbot equipped with a specialized rudder system to address blockages in sewer lines. The IoT-enabled Sewerbot monitors key environmental parameters such as temperature, altitude, and pressure to ensure effective operation. Using computer vision techniques, it can identify and diagnose issues within the sewerage system, while its BLDC motor-operated rudder mechanism is capable of clearing blockages. Additionally, hazardous gas detection is facilitated by MQ sensors, providing real-time alerts for toxic gases, ensuring the safety and efficiency of sewer maintenance operations. This system integrates advanced monitoring and obstruction-clearing capabilities to enhance the maintenance and operational safety of sewer infrastructure.
Simultaneously Localization & Mapping (SLAM) based UGV Design
Abstract: This project focuses on designing an IoT-enabled Unmanned Ground Vehicle (UGV) utilizing Simultaneous Localization and Mapping (SLAM) technology. The UGV is equipped with sensors to monitor environmental parameters such as temperature, altitude, and pressure, allowing for real-time data collection and analysis. The SLAM feature enables the vehicle to autonomously navigate and create a detailed map of its surroundings, enhancing its ability to operate in dynamic environments. This system aims to improve autonomous navigation and mapping capabilities for applications in exploration, surveillance, and environmental monitoring.
IoT and Computer vision-enabled Robotic Manipulator Design
Features: This project introduces an IoT and computer vision-enabled robotic manipulator designed to enhance package handling efficiency through automated color detection. The system utilizes advanced computer vision algorithms to identify and differentiate packages based on color, allowing the manipulator to accurately pick and drop items accordingly. By integrating IoT capabilities, the robotic manipulator can be remotely monitored and controlled, facilitating real-time data sharing and analysis. This innovative approach streamlines logistics operations, making it a valuable asset for various industries, including warehousing and distribution.
IoT enabled Solar Tracking and Monitoring (Prototype)
Abstract: This project presents an IoT-enabled Solar Tracking and Monitoring prototype designed to optimize solar energy production. By utilizing a tracking mechanism, the system follows the sun's path throughout the day to maximize power output from solar panels. It continuously monitors performance metrics and shares real-time data on the IoT Broker "Connect Things," enabling remote access and analysis. This innovative solution enhances solar energy efficiency and provides valuable insights for energy management, making it a significant step towards sustainable energy solutions.
Vision-based State Estimation of an Unmanned Aerial Vehicle
Team Members: Dr. Saiful Azrin B M Zulkifli and Muhammad Irsyad Sahalan
Research Engineer: Ghulam E Mustafa Abro
Sponsored under ICRF Grant No: RG2021-0873
Abstract: This project focuses on developing an indoor logistics delivery drone without relying on GNSS, instead using vision-based state estimation. A monocular camera with fiducial markers is proposed for precise indoor navigation. When the camera captures a marker, its position and orientation are determined via homography transformation, allowing the drone's pose to be inferred from the known marker locations. This method is cost-effective, low power, and reliable, and can be applied to various scenarios such as hospital robots, warehouse logistics, and complex surveillance tasks.
Hand Gesture-based Control of Autonomous UAV for Mobile Disinfectant Dispenser
IEEE HAC & SIGHT Sponsored project under Grant No: 22COVID20
Team Members: Khairel Danish Khairil Anwar, Dr Saiful Azrin B M Zulkifli, Dr. Vijanth Sagayan Asirvadam
Abstract: Autonomous UAVs, or drones, are rapidly advancing and becoming more prevalent, thanks to hybrid algorithms and technologies like AI and machine learning. These drones can perform a range of hazardous, repetitive tasks. This research focuses on developing an autonomous quadrotor UAV equipped with machine learning for object detection and maneuvering, aimed at mitigating COVID-19 spread. The proposed algorithm identifies hand gestures and maintains social distancing while spraying disinfectants, showcasing the potential for commercial UAV applications during health crises.
Development of Range Enhancement Device for Personal Electric Mobility
Independent Project offered by CAREM UTP Malaysia
Abstract: Range anxiety, the fear of running out of energy in electric vehicles (EVs), is a key barrier to their adoption due to limited range, insufficient charging infrastructure, and long charging times. Various solutions have been proposed, including hybrid engines, optimized charging stations, and route-planning algorithms. This research focuses on extending the range of personal electric mobility (PEM) equipment, like electric scooters, by implementing a current-limiting mechanism based on the battery's state-of-charge (SoC). Using MATLAB-Simulink for simulation and Arduino circuitry to regulate motor current, the study aims to optimize battery use and extend the scooter's range.
Load Management System Using LabVIEW
Abstract: The project focuses on developing a Load Management System using LabVIEW, designed to efficiently measure and manage electrical loads. The system features input and output current and voltage measurements, facilitating real-time monitoring and control. It incorporates a graphical user interface (GUI) built with LabVIEW, allowing users to easily manage and switch loads. This system enhances operational efficiency by providing users with precise data for informed decision-making, contributing to optimized load management in various applications.
Computer Vision based Crop Monitoring
Abstract: This project aims to develop a Computer Vision-based Crop Monitoring system focused on wheat growth detection. By analyzing the color and height of wheat plants, the system assesses growth stages and overall health. It integrates temperature and humidity sensors to monitor environmental conditions, while a water sprinkling mechanism automatically adjusts irrigation based on temperature readings. This intelligent system enhances agricultural efficiency by providing timely insights for crop management, ensuring optimal growth conditions and resource utilization.
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