Adaptive control system of header for cabbage combine harvester based on IPSO-fuzzy PID controller

To address the issue of the high rates of cabbage head damage caused by header device parameter mismatches during continuous cabbage harvesting, an adaptive header control system based on an improved particle swarm optimization (IPSO) -fuzzy proportional-integral-derivative (PID) controller was developed. By performing header device kinematic analysis and configuring the system hardware, a negative feedback control model was established for the clamping mechanism lateral displacement and root-cutting mechanism longitudinal displacement. To address the limitations of the standard PSO algorithm, an adaptive inertia weight update method was introduced to balance global exploration and local search capabilities. Additionally, a spiral position update mechanism from the whale optimization algorithm was incorporated to expand the search space. To satisfy the control system requirements for positional accuracy and response speed, the IPSO algorithm was used to optimize the fuzzy PID controller parameters in real-time. Simulation results showed that the IPSO-fuzzy PID controller outperformed traditional PID and fuzzy PID controllers in response speed, steady state, and robustness. Indoor bench tests demonstrated that when the operating speed ranged from 0.1 to 0.5 m/s, the IPSO-fuzzy PID control system achieved an average harvesting acceptance rate of 97.19%, with average lateral and longitudinal displacement errors of 1.31 and 0.92 mm, respectively. The average lateral and longitudinal response times were 0.18 and 0.15 s, respectively. Field experiment results indicated that when the forward speed of the harvester was less than 0.4 m/s, the harvesting acceptance rate for various cabbage varieties exceeded 96.42 %, demonstrating strong robustness and stability. These results confirmed that the IPSO-fuzzy PID control system can effectively adapt to different operating speeds, cabbage varieties, head shapes, and complex field conditions, meeting the industry standards for cabbage harvesting. This finding provides the theoretical support and practical references for precise control in intelligent cabbage harvesting equipment.