考虑路面黏着约束的重型叉车群自适应安全间隔控制方法研究

Research on Adaptive Safety Spacing Control Method for Heavy Forklift Fleets Considering Road Adhesion Constraints

摘要: 新能源重型叉车在动态仓储作业中，由于路面条件(黏着系数)的突变常导致编队控制精度下降及碰撞风险增加。本文针对这一问题，提出了一种基于数据驱动的黏着预见感知与安全间隔自适应调整策略。首先，建立考虑路面黏着限制的重型叉车纵向动力学模型；其次，引入区间泰勒可达性分析，实时解算不同路面条件下的最小安全制动距离；最后，设计了一种改进的无模型自适应协同控制(MFAC)算法，实现了编队间距随路面工况自适应调整。仿真结果表明，该方法在路面黏着系数波动环境下，能够有效缩短编队间距并杜绝碰撞风险，提升作业效率31.86%以上。

Abstract: In dynamic warehousing operations, new energy heavy-duty forklifts often experience reduced formation control accuracy and increased collision risks due to sudden changes in road conditions (adhesion coefficient). To address this issue, this paper proposes a data-driven strategy for adhesion preview perception and adaptive adjustment of safety intervals. First, a longitudinal dynamic model of heavy-duty forklifts considering road adhesion limitations is established. Second, interval Taylor reachability analysis is introduced to compute the minimum safe braking distance under different road conditions in real time. Finally, an improved model-free adaptive cooperative control (MFAC) algorithm is designed to achieve adaptive adjustment of formation spacing according to road conditions. Simulation results demonstrate that this method can effectively reduce formation spacing and eliminate collision risks under fluctuating road adhesion coefficient conditions, improving operational efficiency by more than 31.86%.