摘要
为解决工业生产环境下叉车轨迹跟踪效果精度不高、响应速度慢以及抗干扰能力不佳的问题,提出了一种基于蜣螂优化算法(DBO)的叉车轨迹跟踪PID参数优化框架。该框架基于常用的单舵轮叉车结构,构建了单舵轮叉车精确运动学模型并设计增量式PID控制器架构;引入DBO算法对控制器参数进行智能优化,实现高效精确的PID参数整定;针对控制器至执行器通信链路可能出现的随机故障问题,设计了基于拉格朗日插值法的自适应预测补偿器(ALIP),以提高叉车轨迹跟踪的鲁棒性和抗干扰能力。实验结果表明,与传统的PSO-PID以及手动调参方法相比,DBO-PID方法跟踪精度、抗干扰能力均有提升。在常规工况下,DBO算法的最大误差为0.05 m,收敛步数约为35步,收敛速度相比PSO方法提升22.2%左右。DBO-ALIP方案相较传统方法收敛速度提升60%,且具有更好的轨迹跟踪能力。
In order to solve the problems of low accuracy,full response speed and poor anti-interference ability of forklift trajectory tracking effect in industrial production environment,a PID parameter optimization framework for forklift trajectory tracking with dung beetle optimization(DBO)algorithm is proposed.The framework is based on the commonly used single rudder wheel forklift structure,constructs an accurate kinematic model of single rudder wheel forklift and designs an incremental PID controller architecture;introduces the DBO algorithm to intelligently optimize the controller parameters to achieve efficient and accurate PID parameter tuning;and,for the possible random fault problems in the communication link from the controller to the actuator,a Lagrangian interpolation-based adaptive predictive compensator(ALIP)based on the Lagrangian interpolation method is designed to improve the robustness and anti-interference ability of forklift trajectory tracking.The experimental results show that compared with the traditional PSO-PID and manual parameter adjustment methods,the tracking accuracy and anti-interference ability of the DBO-PID method are improved.Under conventional working conditions,the maximum error of DBO algorithm is 0.05 m,and the number of convergence steps is about 35,which improves the convergence speed by about 22.2%compared with the PSO method.DBO-ALIP scheme improves the convergence speed by more than 60%compared with the traditional method,and it has a better ability of trajectory tracking.
作者
张润麒
钟华庚
张泽龙
张继栋
刘冬
杜宇
ZHANG Runqi;ZHONG Huageng;ZHANG Zelong;ZHANG Jidong;LIU Dong;DU Yu(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China;Ningbo Institute,Dalian University of Technology,Ningbo 315032,China;不详)
出处
《组合机床与自动化加工技术》
北大核心
2025年第12期65-70,共6页
Modular Machine Tool & Automatic Manufacturing Technique
基金
宁波市重点研发计划暨“揭榜挂帅”项目(2023Z042)。
关键词
蜣螂优化算法
叉车轨迹跟踪
PID参数优化
自适应预测补偿
dung beetle optimization algorithm
forklift trajectory tracking
PID parameter optimization
adaptive predictive compensation
