Custom UAV with Model Predictive Control for Autonomous Static and Dynamic Trajectory Tracking in Agricultural Fields

Veera Venkata Ram Murali Krishna Rao Muvva^1,2*Kunjan Theodore Joseph²Yogesh Chawla²Santosh Pitla²Marilyn Wolf²

• ¹Department of Computer Science and Engineering, College of Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
• ²School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States

This study introduces a custom-built uncrewed aerial vehicle (UAV) designed for precision agriculture, emphasizing modularity, adaptability, and affordability. Unlike commercial UAVs that are restricted by proprietary systems, the proposed platform integrates a Cube Blue flight controller for low-level control with a Raspberry Pi 4 companion computer that runs a Model Predictive Control (MPC) algorithm for high-level trajectory optimization. Instead of conventional PID controllers, which often fall short in dynamic or constrained environments, this work adopts an optimal control strategy using MPC. The system also incorporates Kalman filtering to enable adaptive mission planning and real-time coordination with a moving uncrewed ground vehicle (UGV), offering greater flexibility in changing field conditions. The UAV was tested in both simulation and outdoor environments, performing static and dynamic waypoint tracking and complex trajectories such as figure-eight paths under wind disturbances. It consistently achieved root mean square error values between 8 and 20 centimeters during autonomous operations, with slightly higher errors in more complex trajectories. The UAV successfully followed the UGV along nonlinear, curved paths, confirming its suitability for real-world agricultural applications.