# Robust Backstepping Control Applied to UAVs for Pest Recognition in Maize Crops
The robust control Lyapunov function approach is another control approach that has been used to tackle the rejecting bounded-matched disturbance problems in a Planar Vertical Take-Off and Landing (PVTOL) [21], presenting experimental results. Furthermore, using the command filtered backstepping approach with a parameter scheduling algorithm, an experimental flying test of a quadrotor with a nonlinear controller in an indoor environment was described in [22]; however, the robust design was not included in the controller synthesis. Simulation routines of a robust control based on a backstepping method was presented in [23] to drive the position and attitude of a unmanned mini aerial rotorcraft vehicle subjected to bounded uncertainties and bounded disturbances. According to the specialized literature cited above, most of the robust backstepping or robust control algorithms were tested in simulations: [7; 8; 9; 10; 15; 16; 19; 20; 23]; advanced control in an outdoor environment: for nitrogen estimation with relative high-cost equipment was presented in [12], and robust landing algorithm onto a heaving platform in [18]; in an indoor environment in UAVs [13; 14; 17] and in a PVTOL [21]. In our opinion, a robust backstepping control algorithm has to be sufficiently simple to implement it on an autopilot for flying tests in a more realistic environment (outdoors and trajectory tracking in precision agriculture). In this sense, the embedded computing resources required by autonomous unmanned aircraft systems represented a challenge [24]. The backstepping algorithm allows to synthesize a recursive stabilizing controllers for nonlinear systems and it could be modified to obtain robust controllers. Moreover, unmodeled dynamics, external disturbances, actuators, and signal conditioner nonlinearities are latent in the real-time control loop. Under all these conditions, it is essential to assess the controller's performance [25] by experimental evaluation of the control algorithms in meaningful environments to validate their robustness in a closed loop with a real plant. In this contribution, based on the Lyapunov approach, a robust nonlinear controller solves the trajectory tracking problem for a quadrotor. Our proposal uses the backstepping method [26], allowing to construct an iterative control algorithm that rejects the effects of the unstructured dynamics in the quadrotor nonlinear model. For this purpose, the quadrotor model is divided into four subsystems related to the altitude, the yaw angle, the pitch-\(x\) and the roll-\(y\), as in [11], and it considers the coupled nonlinear dynamics in the actuators. While these disturbances are unknown, they are assumed as bounded matched vanishing dynamics. The robustness is incorporated in the proposed controller to stabilize the drone in the presence of non-modeled dynamics, which improve the image capture system on board of the UAV as it flies over corn crops to detect dry leaves, providing clues to identify fungus such as Phylachora maydis, Monographella maydis, and Coniothyrium phyllachorae on the corn leaves. These fungi are the cause of the tar spot in the corn [27], which causes crop losses. Regarding the proposal mission in this paper, growing loss owing to disease is one of the most representative problem in agriculture. Crop growth inspecting and early identification of pest in the crops is still an important issue. In this regard, farmers are investing great efforts to conserve crops; however, they are mostly failing because they are not correctly monitoring the crops when they have been infested. Additionally, plagues in the crops are also difficult to detect due to it is not uniformly distributed. Hence, UAVs have a key role in crop disease surveillance and early detection [28; 29; 30]. This research attempts to provide experimental evidence of the implementation feasibility of the developed robust backstepping control in a PixHawk autopilot used in a pest detection task.