Research and Experimental Evaluation of an Electric-Drive Maize Precision Seeding System Integrating Physics-Informed Neural Networks and PID Control

To address the problem of decreased rotational speed tracking accuracy of the seeding motor caused by system nonlinearity, time-varying parameters, and field disturbances during high-speed electric-driven precision seeding, a PINN-PID control method was proposed. First, the kinematic relationship among operating speed, target plant spacing, and seeding motor rotational speed was established, and a motor dynamics model was constructed. Subsequently, the data supervision error, seeding kinematic residual, motor dynamics residual, and smoothness constraint were jointly embedded into the neural network loss function, and the network prediction results were combined with PID feedback regulation. Test results showed that the RMSE and MAPE of PINN rotational speed prediction are 0.7697 ± 0.0422 r/min and 0.640 ± 0.040%, respectively, with a coefficient of determination R2 of 0.99714 ± 0.00047. In simulation tests, the step response overshoot and settling time of PINN-PID were 0.17% and 0.13 s, respectively, which were 66.28% and 63.89% lower than those of BP-PID. In bench tests, PINN-PID achieved a 97.41% mean qualification index, gaining 3.56, 2.13, and 0.79 percentage points over conventional PID, fuzzy PID, and BP-PID, respectively. Constant-speed field trials yielded 95.13% qualification and 13.90% coefficient of variation; under stepped-speed conditions, the values were 95.37% and 14.05%, representing a 2.00-percentage-point qualification gain and a 6.49% coefficient of variation reduction versus BP-PID. Fusing physical constraints with data-driven learning improved motor state prediction and disturbance adaptability, offering a reliable, physically interpretable control approach for high-speed electric precision seeding.