Rotor Speed Prediction Model of Multi-Rotor Unmanned Aerial Spraying System and Its Matching with the Overall Load

During continuous spraying operations, the liquid in the pesticide tank gradually decreases, and the flight speed changes as the route is altered. To maintain stable flight, the rotor speed of a multi-rotor unmanned aerial spraying system (UASS) constantly adjusts. To explore the variation law of rotor speed in a multi-rotor UASS under objective operation attributes, based on indoor and outdoor experimental data, this paper constructs a mathematical model of the relationship between rotor speed and thrust. The model fitting parameter (R²) is equal to 0.9996. Through the neural network, the rotor speed prediction model is constructed with the real-time flight speed and the payload of the pesticide tank as the input. The overall correlation coefficient (R²) of the model training set is 0.728, and the correlation coefficients (R²) of the verification set and the test set are 0.719 and 0.726, respectively. Finally, the rotor speed is matched with the load of the whole UASS through thrust conversion. It is known that the single-axis load capacity under full-load state only reaches about 50% of its maximum load capacity, and the load increase is more than 75.83% compared with the no-load state. This study provides a theoretical and methodological reference for accurately predicting the performance characterization results of a power system during actual operation and investigating the dynamic feedback mechanism of a UASS during continuous operation.