基于模型参数学习的调节阀最优控制策略  

Optimal control strategy of control valve based on model parameter learning

作　　者：张博 江爱朋[1] 姜家骥 祁雁英 薛立 王浩坤[1] Zhang Bo;Jiang Aipeng;Jiang Jiaji;Qi Yanying;Xue Li;Wang Haokun(School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学自动化学院,杭州310018

出　　处：《电子测量技术》2022年第15期41-47,共7页Electronic Measurement Technology

基　　金：浙江省自然科学基金一般项目(LY20F030010);国家自然科学基金面上项目(61973102)资助。

摘　　要：为提高智能气动调节阀的控制性能,本文根据气动执行机构的建模分析,提出一种基于模型参数自学习的调节阀最优控制策略。首先,建立气动执行装置的动力学模型,并对五步开关控制算法进行分析。其次,基于模型设计最优控制所需要的控制参数自学习策略。最后,根据参数自学习获得的控制参数,对五步控制方法进行改进,给出一种优化控制策略及实施步骤。实验结果表明,所提优化控制算法控制过程中无明显超调产生,震荡显著减弱。控制精度明显提高,调节时间显著缩短,其中小行程平均调节时间缩短了38.1%,平均误差减小了61.4%;大行程平均调节时间缩短了38.7%,控制精度提高了39.4%。In order to improve the control performance of the intelligent pneumatic control valve, this article was based on the modeling analysis of the pneumatic actuator, A optimal control strategy of control valve based on model parameter learning was proposed. Firstly, the dynamic model of the pneumatic actuator was established, and the five-step switch control algorithm was analyzed. Secondly, the control parameter self-learning strategy required for optimal control was designed based on the model. Finally, according to the control parameters obtained by parameter self-learning, the five-step control method was improved to give an optimized control strategy and implementation steps. The experimental results show that there is no obvious overshoot in the control process of the proposed optimal control algorithm, and the oscillation is significantly weakened. The control accuracy is significantly improved, and the adjustment time is shortened. The average adjustment time of small strokes is shortened by 38.1%, and the average error is reduced by 61.4%.The average adjustment time of large stroke is shortened by 38.7%, and the control accuracy is improved by 39.4%.

关 键 词：调节阀 气动 最优控制 参数自学习 机理模型 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]