A DUNG BEETLE OPTIMIZED MPC ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION OF UNMANNED AGRICULTURAL VEHICLE

This paper presents a multi-objective optimization approach for unmanned agricultural vehicles operating in complex farmland environments. To overcome the limitations of traditional Model Predictive Control (MPC) and heuristic algorithms, a Dung Beetle Optimization-based MPC (D-MPC) multi-objective optimization method is proposed. Specifically, a kinematic model of the unmanned agricultural vehicle is established, incorporating the operational characteristics of complex farmland conditions. The Dung Beetle Optimization (DBO) algorithm is integrated into the MPC framework to enhance performance by leveraging the population-based search behavior of dung beetles. This integration improves both control accuracy and computational efficiency by dynamically adjusting control inputs based on real-time motion predictions, enabling more precise trajectory optimization. Experimental validation is conducted through a dual-verification approach, including both simulation and real-vehicle tests. The results indicate that, compared with conventional control methods, the proposed approach improves trajectory tracking accuracy by approximately 50% and 75% in two representative simulation scenarios, while increasing the battery State of Charge (SOC) by 0.1% and 0.12%, respectively. In real-vehicle experiments, trajectory tracking accuracy is improved by 70%, and SOC is increased by 0.015%.