Despite the increasing use of unmanned aerial vehicles (UAVs) for pest control, there are still possibilities to optimize the spraying process by selecting the flight parameters of the operations involved, such as flight speed and height, according to the penetration of droplets that is sought to have in the corn plant. In this research, the flight parameters that the operators should select according to the type of disease they treat in corn crops were determined. To achieve this, first, a survey of the main diseases that attack corn crops in the region was carried out along with their location in the plant. Subsequently, flight tests were carried out selecting speeds of 2.80, 4.75, and 5.97 m/s and heights of 2.00, 2.75, and 3.50 m. The plants’ cover rates at the three different heights (top, middle, and bottom) were collected by hydrosensitive strips and scanned by the SnapCard scanning software. The use of statistical techniques allowed understanding the impact that flight parameters had on the coverage rate and droplet penetration: the higher the speed, the lower the coverage rate in the upper part of the plant and the higher the coverage rate in the lower part. On the other hand, the lower the height, the higher the coverage rate and droplet penetration.

Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in various applications, ranging from surveillance to package delivery. Achieving precise control of UAV position while enhancing robustness against uncertainties and disturbances remains a critical challenge. In this study, we propose a robust nonlinear control system for a UAV and its actuators, focusing on accurately controlling the position reference vector and improving robustness against parameter uncertainties and external disturbances. The control strategy employs two control loops: an outer loop for the UAV frame and an inner loop for the UAV actuators. The outer loop generates the required angular velocities for the actuators to follow the reference position vector using the UAV’s output and the inner loop ensures that the actuators track these angular velocity references. Both control loops utilize PI-like controllers for simplicity. The proposed system incorporates nonlinear control techniques and estimation strategies for disturbances and parameter variations, enabling dynamic adaptation to changing environmental conditions. Numerical simulations were performed using both Simulink^® and the simulated PX4 Autopilot environment, showing the effectiveness of the proposed control system in achieving precise position control and robust performance for both the UAV and its actuators in the presence of uncertainties and disturbances. These results underscore the potential applicability of the control system in other UAV operational scenarios.

This paper introduces a robust formation control strategy for Unmanned Aerial Vehicles (UAVs) designed to maintain coordinated trajectories and relative positioning in three-dimensional space. The proposed methodology addresses the challenges of parametric uncertainties and external disturbances by employing a backstepping-based framework with integrated proportional-integral virtual controls. The control strategy stabilizes tracking errors in the x, y, and z axes, ensuring that the UAVs maintain a cohesive formation even in the presence of dynamic model variations and environmental perturbations. The approach combines dynamic models of UAV motion, incorporating translational and rotational behaviors, with a decomposition of relative distances in the leader’s local reference frame to ensure precise formation control. This framework enhances stability, trajectory tracking, and disturbance rejection. Validation through MATLAB-Simulink simulations demonstrates the effectiveness of the proposed strategy, showcasing its ability to maintain formation and trajectory adherence under diverse operating conditions. The results emphasize the robustness and flexibility of the control approach, making it suitable for demanding applications requiring precise multi-UAV coordination.

In this paper, a methodological framework employing an observer-based nonlinear controller is presented for controlling the lateral velocity of a farm tractor, as well as the yaw velocity of the agricultural implement. This approach relies on measurements obtained from sensors installed on a modern farm tractor, including lateral and longitudinal accelerations, longitudinal velocity, yaw rate, steering angle, and the differential yaw rate between the farm tractor and the implement. The nonlinear observer estimates the longitudinal and lateral velocities of the vehicle, as well as the roll dynamics of the implement, and ensures the exponential convergence of the observed variables. The control objective is formulated to ensure error feedback control, guaranteeing accurate tracking of the lateral velocity and yaw rate of the farm tractor and implement, following the reference patterns for these variables. The reference system is modeled as an “ideal” tractor operating without attachments. To evaluate the proposed controller’s performance, two test maneuvers were conducted. The first test involved the classic U-turn maneuver, commonly executed by tractors, while the second was a double-step maneuver, a standard in ground vehicle testing. Both maneuvers were simulated using MATLAB–Simulink to evaluate the controller’s effectiveness and robustness against parameter variations.