DEEP LEARNING-DRIVEN PREDICTIVE CONTROL METHOD FOR OPTIMIZING COMBINE HARVESTER OPERATION SPEED

ABSTRACT To enhance the automation and efficiency of combine harvesters, this paper proposes a predictive control method based on Long Short-Term Memory (LSTM) neural networks. The method integrates multi-sensor data fusion using an Extended Kalman Filter (EKF) to improve speed measurement accuracy. By considering feeding volume, operational performance indicators, and critical component speeds, an LSTM-based model predicts the optimal operation speed. The predicted speed is then regulated through an incremental proportional-integral-derivative (PID) control control system. Simulation and field experiments validate the effectiveness of the proposed approach, demonstrating improved speed stability and work efficiency. The results indicate that the system enhances operational performance and reduces manual intervention, contributing to the advancement of intelligent agricultural machinery.