Research on inter-plant weeding control in peanut at LADRC based on IACO-PSO optimization

In the complex operating environments encountered in the field, control system parameters are often difficult to adjust, leading to issues such as excessive overshoot and poor anti-interference performance. These challenges make mechanical seedling avoidance and inter-plant weeding between peanut plants problematic, increasing the risk of seedling damage. To address this, this study investigated a Linear Active Disturbance Rejection Control (LADRC) system for peanut inter-plant weeding, optimized using an improved Ant Colony Optimization-Particle Swarm Optimization IACO-PSO algorithm. By controlling the angular velocity of the stepper motor, we were able to regulate seedling avoidance along the weeding knife’s operational path. A mathematical model for inter-plant seedling avoidance and weeding was established, and an improved hybrid algorithm combining the ant colony algorithm and particle swarm optimization was proposed to optimize the key parameters of the LADRC system. Benchmark function comparisons demonstrated that the improved algorithm offers a superior optimization performance and stability. Simulation experiments were then carried out to evaluate the control performance of the system in the inter-plant weeding scenario. The results show that, compared to other algorithms, the hybrid IACO-PSO algorithm exhibits faster convergence speeds and higher accuracy, significantly enhancing the system’s overall control performance. In particular, the IACO-PSO optimized control system reduced recovery times from disturbances by 96.6%, 75%, 82%, and 64.3%, respectively. These findings highlight the system’s strong anti-interference capability, robustness, and improved response speed, making it a highly effective solution for peanut inter-plant weeding.