System Identification and Control Modeling of 6-Rotor Unmanned Air Craft for Wildfire Spread Monitoring
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Authors
Karakurt, Tolga
Issue Date
2024
Type
Thesis
Language
Keywords
DJI M600 PRO , Model Predictive Control , System Identification , Wildfire Monitoring
Alternative Title
Abstract
Wildfires are natural occurrences that have been significantly acknowledged as devastating natural disasters globally. The fuel type, temperature, wind and humidity are some of the elements that directly affect the orientation and propagation of the fire in the area. For tracking and monitoring purposes, traditional methods on the ground and in the air constitute great safety risks, especially considering unpredictable fire behavior. Therefore, there is an increasing need for novel options for field monitoring approaches to minimize risk to reach real-time information. The technology to reduce the impacts of wildfires involves the use of aerial unmanned systems in order to leverage efficient mapping and real-time surveillance. This thesis aims to identify the system dynamics and develop a control strategy for tracking wildfire spread in vast green areas using the DJI Matrice 600 Professional 6-rotor unmanned air vehicle (UAV). Flight data is collected to identify and generate the mathematical model of the aircraft in each axis motion. Dynamic identification is conducted through a time-domain dataset in the Matlab toolbox, driving the Model Predictive Control algorithm. Sensory information is processed on a high-level onboard computer running a Robot Operating System (ROS). In this work, we contribute system identification, modeling, and control for commercial grade drones. MPC is implemented as the preferred model to resolve proper flight routes for UAV missions. For position control, the linearized dynamics of the air vehicle has been deployed at the core. Through both experiment and simulation studies, the proposed autonomous aerial vehicle's performance has been demonstrated. Aerial wildfire monitoring in high-risk areas can be carried out without human intervention using the proposed technique, which is considered achievable.