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作 者:贾权 郭计云 徐青云[2] Jia Quan;Guo Jiyun;Xu Qingyun(School of Mechanical and Electrical Engineering,Shanxi Datong University,Datong 037003,China;School of Coal Engineering,Shanxi Datong University,Datong 037003,China)
机构地区:[1]山西大同大学机电工程学院,山西大同037003 [2]山西大同大学煤炭工程学院,山西大同037003
出 处:《锻压技术》2021年第3期167-173,共7页Forging & Stamping Technology
基 金:2020年大同市科技计划立项资助项目(2020022);2018年大同市科技计划立项资助项目(2018104);山西大同大学2017年度科研基金资助项目(2017K8)。
摘 要:为了提高轧机多电机传动系统的同步性,提出了参数自学习PID与状态观测的组合控制方法,分析了轧机主传动系统的工作原理,建立了电机-轧辊的二质量旋转体模型。设计了参数自学习PID控制的同步控制器,基于BP神经网络进行参数自学习,实现了实时的最佳PID控制。为了消除传动轴扭振引起的系统震荡,设计了状态观测器,对轧辊转矩进行预先估计和补偿;同时,将状态观测值进行反馈,实现极点配置,获得了期望的极点位置和控制性能。经验证,与单独使用参数自学习PID控制器相比,组合控制器在启动阶段的调节时间降低了81.8%;在受扰阶段的最大同步误差降低了87.4%,调节时间减少了29.3%。以上数据说明PID与状态观测的组合控制器能够实现较好的同步控制效果,且抗干扰能力较好。In order to improve the synchronization of multi-motor driving system for rolling mill,a combined control method of parameter self-learning PID and state observation was proposed,the working principle of the main driving system of rolling mill was analyzed,and a motor-roll two-mass rotating body model was established. Then,a synchronism controller for parameter self-learning PID was designed,which was based on BP neutral network for parameter self-learning and realized the best real-time PID control. In order to eliminate the system shock caused by torsional vibration of driving shaft,a state observer was designed to estimate and compensate the roll torque in advance. Meanwhile,the state observation value was fed back to realize the pole configuration and obtain the desired pole position and control performance. It is verified that compared with using the parameter self-learning PID controller alone,the adjusting time of combined controller in the startup stage decreases by 81. 8%,and the maximum synchronization error in the disturbed stage decreases by 87. 4%,and the adjusting time decreases by 29. 3%. The above data shows that the combined controller of PID and state observation achieve better synchronization control effect and better anti-interference ability.
关 键 词:轧机传动系统 同步控制 参数自学习PID控制 状态观测 极点配置
分 类 号:TP273[自动化与计算机技术—检测技术与自动化装置]
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