Adaptive State Prediction for Operating Point Optimization of Agricultural Machine Combinations

Improving the efficiency of agricultural machinery is increasingly important. This work presents a constrained optimization approach for operating point optimization of agricultural machine combinations. System states are adaptively predicted under varying conditions to identify advantageous operating points while satisfying process constraints. Validation with multiple tractor-cultivator combinations demonstrates the prediction accuracy and efficiency improvement.