Disturbance rejection control of the agricultural quadrotor based on adaptive neural network

In order to solve the problem of stability of agricultural quadrotor working, its controller designing is the first priority. Therefore, this paper makes an attempt to use the Radial Basis Function (RBF) neural network adaptive method combined with sliding mode control to control its height channel. Validation of the efficacy of the RBF neural network in control is conducted through simulation experiments utilizing quadrotor parameters. The application of the method to the control of agricultural quadrotor has laid a theoretical foundation. At the same time, through simulation experiments, it is concluded in theory that the RBF neural network can have a good prediction and elimination effect on the interference during the flight, and the change of the time constant will not affect the control effect of the aircraft. Notably, abrupt changes in time constant indicate UAV motor malfunction. Simulation results affirm the efficacy of the proposed control method in regulating UAV altitude and addressing sudden faults. Real-world experimentation (vegetable field including bean, pepper, eggplant, tomoto, etc.) reveals that even when UAV propellers sustain damage to a certain extent, altitude control and hover capabilities remain intact. These findings provide a solid groundwork for subsequent altitude control endeavors in agricultural quadrotor operations, while also offering innovative avenues for advancing the field.