南京工业大学学报（自然科学版）2025，Vol.47Issue(6)：668-675,8.DOI:10.3969/j.issn.1671-7627.2025.06.006
基于IPSO-BP-PID的四缸塔式起重机同步控制研究
Synchronization control of four-cylinder tower cranes based on IPSO-BP-PID
摘要
Abstract
To address the poor stability in single-cylinder tower cranes during hoisting,caused by uneven loads and external disturbances,which limits their ability to meet the high-precision and high-reliability requirements of large-scale engineering projects,this study develops a hydraulic control system suitable for four-cylinder tower cranes.A control strategy is proposed that integrates mean deviation coupling control with real-time PID tuning based on a backpropagation(BP)neural network optimized by an improved particle swarm optimization(IPSO)algorithm.By optimizing the initial weights and thresholds of the BP neural network with IPSO,the PID parameters are dynamically adjusted,enhancing both the synchronization accuracy and dynamic response of the multi-cylinder system.A co-simulation platform integrating AMEsim and Simulink was developed to validate the proposed strategy,based on a hydraulic circuit model and a mathematical model of a dual-cylinder system.Simulation results indicate that the maximum synchronization error of the four-cylinder system is 1.45 mm,while the error after stabilization is maintained within 0.50 mm,and the response speed of the system is significantly improved.The proposed framework of mean deviation coupling control combined with the IPSO-BP-PID controller demonstrates excellent robustness and disturbance rejection during the hoisting process of four-cylinder tower cranes,providing an effective solution for high-precision multi-cylinder synchronization control.关键词
塔式起重机/同步控制/均值偏差耦合控制/BP神经网络/粒子群优化算法Key words
tower crane/synchronization control/mean deviation coupling control/BP neural network/improved particle swarm optimization分类
机械制造引用本文复制引用
王子路,殷晨波,马守磊,杨超,刘星宇..基于IPSO-BP-PID的四缸塔式起重机同步控制研究[J].南京工业大学学报（自然科学版）,2025,47(6):668-675,8.基金项目
国家重点研发计划(2021YFB2011904) （2021YFB2011904）
