GAPSO-Optimized Fuzzy PID Controller for Electric-Driven Seeding

To improve the seeding motor control performance of electric-driven seeding (EDS), a genetic particle swarm optimization (GAPSO)-optimized fuzzy PID control strategy for electric-driven seeding was designed. Since the parameters of the fuzzy controller were difficult to determine, two quantization factors were applied to the input of the fuzzy controller, and three scaling factors were introduced into the output of fuzzy controller. Genetic algorithm (GA) and particle swarm optimization (PSO) were combined into GAPSO by a genetic screening method. GAPSO was introduced to optimize the initial values of the two quantization factors, three scaling factors, and three characteristic functions before updating. The simulation results showed that the maximum overshoot of the GAPSO-based fuzzy PID controller system was 0.071%, settling time was 0.408 s, and steady-state error was 3.0693 x 10(-5), which indicated the excellent control performance of the proposed strategy. Results of the field experiment showed that the EDS had better performance than the ground wheel chain sprocket seeding (GCSS). With a seeder operating speed of 6km/h, the average qualified index (I-q) was 95.83%, the average multiple index (I-mult) was 1.11%, the average missing index (I-miss) was 3.23%, and the average precision index (I-p) was 14.64%. The research results provide a reference for the parameter tuning mode of the fuzzy PID controller for EDS.

Automated summary

This study proposes an optimized fuzzy PID control strategy for electric-driven seeding using genetic particle swarm optimization (GAPSO). The simulation results demonstrate excellent control performance, while field experiments show that electric-driven seeding outperforms ground wheel chain sprocket seeding.