摘要
装片机运动控制平台主要采用二自由度的PID控制策略,其控制性能依赖于平台模型精度,但平台模型存在参数不确定问题,导致PID控制参数需要结合实际进行人工调参,难以保证控制指标要求。针对这一问题,提出了一种基于机理模型和实际平台测试数据相结合的模型参数辨识方法,并采用粒子群算法优化辨识后的模型参数。与实际平台阶跃测试数据比较,在大位移(高于50 mm)下,未优化的辨识模型动态响应误差达到31.83%,稳态误差达到28.64%,而优化后的辨识模型动态响应误差不超过3.66%,稳态误差不高于1.2%。故采用粒子群优化算法显著提高了模型的辨识精度,为后续的高性能二自由度PID控制器设计奠定了基础,对基于模型的其他类型运动控制器设计具有重要意义。
The motion control platform of chip loading machine mainly adopts two-degree-of-freedom PID control strategy,and its control performance depends on the accuracy of platform model,but the platform model has the problem of parameter uncertainty,which leads to the PID control parameters need to be manually adjusted in combination with the actual,and it is difficult to ensure that the control index requirements.Aiming at this problem,a model parameter identification method based on the combination of the mechanism model and the actual platform test data is proposed,and the particle swarm algorithm is used to optimize the identified model parameters.Comparing with the actual platform step test data,the dynamic response error of the unoptimized identification model reaches 31.83%and the steady-state error reaches 28.64%under large displacement,while the dynamic response error of the optimized identification model is no more than 3.66%and the steady-state error is no more than 1.2%.Therefore,the use of particle swarm optimization algorithm significantly improves the accuracy of model recognition,which lays the foundation for the subsequent design of high-performance two-degree-of-freedom PID controller,and isof great significance for the design of other types of model-based controllers.
作者
张翔
花国祥
朱友为
钱承山
陈怀荣
梁伟明
ZHANG Xiang;HUA Guoxiang;ZHU Youwei;QIAN Chengshan;CHEN Huairong;LIANG Weiming(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Automation,Wuxi University,Wuxi 214000,China;不详)
出处
《组合机床与自动化加工技术》
北大核心
2025年第4期23-26,31,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
江苏省基础研究计划自然科学基金-青年基金项目(BK20230173)。
关键词
装片机
运动控制平台
机理模型
参数辨识
粒子群优化
chip loading machine
motion control platform
mechanism model
parameter identification
particle swarm optimization
