Projects

Project title: Advancing Microgrid and Renewable Energy Research with advanced research facility

Role: Project PI

Duration: 12 months (August 2024 - July 2025)

Description:
The project aims to upgrade the present research lab to develop and demonstrate the functions of a Distribution System Operator (DSO) for optimizing the utilization and management of Distributed Energy Resources (DER). The project involves interfacing with DER control systems and microgrid control systems. Additionally, it includes an in-depth analysis of prototype feeders with a high penetration of energy storage. This project is in steadfast alignment with the ambitious goals of Saudi Arabia's Vision 2030, which seeks to transform the nation's energy sector and foster innovation. By developing and demonstrating advanced DSO functions for the efficient management of Distributed Energy Resources (DER) and microgrids, this project actively supports Vision 2030's objectives of achieving energy efficiency, enhancing sustainability, and advancing technological capabilities. Our commitment is to contribute to realizing a diversified and sustainable energy landscape, aligning with the broader vision of a prosperous and vibrant Saudi Arabia.

Project title: Robust control strategies for trajectory tracking of the Internet-of-Drones 

Role: Project Co-PI

Duration: 12 months (September 2024 - August 2025)

Description:
This project aims to promote the emerging technology of the Internet-of-Drones and its applications in the Kingdom of Saudi Arabia. The Internet-of-Drones allows remote monitoring and control of drone system over the Internet and has a wide range of practical military and civilians’ applications. The inclusion of the Internet in the control loop brings constraints to the control system such as latency, quantization error and channel bandwidth limitations. These constraints adversely affect the stability and performance of the Internet-of-Drones. In this year project, we plan to design an industrial networked control system using the Internet-of Drones for a real-time objective tracking purpose. First, we will deeply study the dynamic of the Internetof-Drones and develop a comprehensive model for such complex system. Accordingly, we will design robust trajectory tracking control strategies using the Internet-of-Drones. The nonlinear controllers, to be proposed. in this project, combine back-stepping, sliding mode and feedback linearization controllers. Fractional order derivatives and integrals provide more accurate models for complex systems in comparison with the classical integer derivatives and integrals. In this project, fractional order will be used as an effective tool to model and design control strategies for the Internet-of-Drones. In this framework, we intend to extend our models for the Internet-of-Drones to accommodate the fractional order dynamic systems and then implement fractional-order proportional-integrator-differentiator (PID) and fractional sliding mode controllers for trajectory tracking purpose.

Project title: Vision-Guided Precise Landing of Quadcopters Using Open-Source Flight Controllers and IoT Integration  

Role: Project PI

Duration: 12 months (December 2024 - November 2025)

Description:
This project focuses on the development and implementation of a vision-guided precise landing system for quadcopters using open-source flight controllers and IoT integration. The primary objective is to enhance the accuracy of autonomous landing by leveraging computer vision and real-time data from onboard sensors. Utilizing an open-source platform provides flexibility for customization, cost efficiency, and compatibility with a wide range of hardware components. The system employs a combination of visual markers and camera feedback to enable the quadcopter to detect, navigate, and land on a specified target with high precision. The integration of IoT systems allows for efficient data transmission between the quadcopter, ground control, and external sensors, ensuring real-time monitoring and adjustments to flight trajectories. By using open-source flight controllers like PX4 or ArduPilot, the project takes advantage of community-supported, adaptable platforms that enable the development of customized control techniques. The vision system processes image data through computer vision algorithms and edge detection to accurately localize the landing pad. IoT technology is integrated to facilitate communication between the drone and the ground station, optimizing real-time feedback and control.