Intelligent Control of Sugarcane Seeder Planting System Through Fuzzy-PID Simulation

Abstract

To address the challenges of low operational efficiency and poor uniformity in seed distribution commonly observed in traditional sugarcane sowing machines, this study proposes an intelligent seed distribution system based on fuzzy PID control. An integrated modeling approach combining multi-body dynamics (MBD) and discrete element method (DEM) simulations was employed to systematically construct the dynamic mathematical model of the sowing mechanism. Specifically, the dynamic equations of the seed distribution mechanism were first derived using MBD theory, followed by the extraction of seed flow characteristic parameters through DEM simulation. Subsequently, a secondorder lag system transfer function was obtained via parameter identification. To overcome the inherent limitations of conventional PID control—such as large overshoot and extended adjustment time—a real-time fuzzy rule-based optimization strategy for PID parameters was developed. This strategy enables dynamic adjustment of proportional, integral, and derivative gains, thereby achieving high-precision closed-loop control of sowing accuracy. Simulation results confirm that the proposed fuzzy-PID control method significantly enhances adaptive control performance compared with traditional PID control. By coupling intelligent control algorithms with agronomic parameters, the study not only demonstrates improved sowing precision for sugarcane but also establishes a theoretical foundation and technical framework for the precise planting of other economic crops. This work offers valuable engineering significance and application potential for advancing the intelligent development of modern agricultural machinery