# Automatic Control Optimization for Large-Load Plant-Protection Quadrotor
## 1 Introduction  
Quadrotor unmanned aerial vehicles (UAVs) have been widely used in many areas because of their maneuverability and flexibility, as Reference [1] summarized. In plant protection areas, quadrotor UAVs are helping reducing agriculture damage [2]. However, quadrotors are high-dimensional nonlinear underactuated systems with different physical parameters, which increases the difficulty of stable control. Some control algorithms have been tested and applied to quadrotors, and the most widely used algorithm is the traditional proportional-integral-derivative (PID) controller, which achieves an acceptable flight performance. Projects [3, 4] are popular open source flight controllers using PID-based control algorithms; the users must tune PID parameters for each quadrotor platform. Another research effort [5] made many improvements to the PID controllers, and the Smith predictor and rotational trajectory planner were augmented. The linear quadratic regulator (LQR) algorithm has also made progress in the control of quadrotor UAVs. One study [6] linearized the quadrotor system and applied the LQR method to quadrotors and energy consumption was also considered. Another report [7] presented a morphing quadrotor, where the arms of the quadrotor could rotate around the body using servos. To achieve stable flight at any configuration, the researchers exploited an LQR-based control strategy that adapts to the drone morphology in real time. However, the performance of these linear control algorithms becomes worse with a larger attitude because the model errors are larger under this condition. Studies [5, 7] addressed this problem and compensated for the model errors in their attitude control loops. Many studies have tested nonlinear control algorithms on quadrotor UAVs. One study [8] achieved stable control of a quadrotor with the backstepping algorithm, and this control method was found to control the quadrotor in any state without nonlinear compensation. Backstepping and sliding mode techniques were applied to an indoor micro quadrotor in another study [9]. A further study [10] developed sliding mode controllers for the attitude loop and the position loop separately. H-infinity control was found to achieve robust control for quadrotors in another study [11], and the effect of external disturbances was minimized. An additional study [12] introduced observers to further compensate for these external disturbances. The present article focuses on the attitude control of large-load plant-protection quadrotors. Large-load plant-protection quadrotors can be defined as quadrotors equipped with sprayers and a tank containing a large amount of water or pesticide (the mass of water or pesticide can be even heavier than the quadrotors themselves) and the quadrotors can water plants or spray pesticide during flight. Compared to the control of common small quadrotors, some factors need to be considered in the control of large-load plant-protection quadrotors. First, the water in the tank gradually diminishes during application, so the physical parameters change during this process. The mass and moment of inertia (MOI) of the three axes of a plant-protection quadrotor with an empty tank or with a full tank can be very different. The controller must learn these changes because the flight performance of the plant-protection quadrotor can be severely affected. One report [13] presents a fuzzy PID method to solve this problem on plant-protection quadrotor, the PID parameters were adjusted in real time using a fuzzy control table. This problem also appears in the control of the morphing quadrotor in the study [7], and was solved using the adaptive LQR method. Deterministic artificial intelligence (DAI) is a novel intelligent control algorithm that can automatically enforce optimality without any controller tuning. This method has been applied to unmanned underwater vehicles (UUV) [14] and spacecraft [15], and is also able to solve the changing-MOI problem.