实时参数整定的无模型自适应控制算法及其在气体分馏装置的应用  

MODEL-FREE ADAPTIVE CONTROL WITH REAL-TIME PARAMETER TUNING AND ITS APPLICATION IN GAS FRACTIONATION UNIT

作　　者：谷小峰 马庆鲁 黄文杰 李国庆[1] Gu Xiaofeng;Ma Qinglu;Huang Wenjie;Li Guoqing(School of Chemistry&Chemical Engineering,South China University of Technology,Guangzhou 510641;Shandong Chambroad Petrochemicals Co.,Ltd.)

机构地区：[1]华南理工大学化学与化工学院,广州510640 [2]山东京博石化公司

出　　处：《石油炼制与化工》2024年第3期97-106,共10页Petroleum Processing and Petrochemicals

摘　　要：现有无模型自适应控制(MFAC)算法中,4个模型参数λ,ρ,η,μ在控制过程中保持不变,导致伪偏导对控制进程影响小、算法自适应能弱等问题。利用径向基函数(RBF)神经网络,基于系统的输入和伪偏导,以期望输出与实时输出差值为训练误差的实时整定参数,提高了MFAC的自适应能力;进而提出了一种新的离散时间非线性系统紧格式动态线性化MFAC算法(简称BRF-MFAC算法),并通过非线性系统控制案例验证了RBF-MFAC良好的跟踪性能;将其应用于某炼油厂0.3 Mt/a气体分馏装置,相比现有MFAC算法,丙烯塔单输入单输出(SISO)系统丙烯产品纯度达标操作调整次数减少42.4%,多输入多输出(MIMO)系统丙烯产品纯度和产量达标操作调整次数减少78.0%。In the existing model free adaptive control(MFAC)algorithms,the four model parametersλ,ρ,η,μare kept constant during the control process,which leads to the problems of small influence of pseudo-partial derivatives on the control process and weak adaptive energy of the algorithm.Using the radial basis function(RBF)neural network,based on control input and pseudo partial derivatives,and taking the difference between the expected output and the real-time output as the training error,the four parameters can be adjusted in real time,which improves the existing compact format dynamic linearization MFAC method for discrete time nonlinear systems.Furthermore,a new BRF-MFAC algorithm was proposed,and its superiority of tracking performance was verified in the control of a nonlinear system.Compared with MFAC,the operation adjustment time of RBF-MFAC system for propylene concentration could reduce by 42.4%in the propylene separation column of a 0.3 Mt/a gas fractionation unit.The operational adjustment time of propylene product concentration and production in MIMO system could reduce by 78.0%.

关 键 词：过程控制 神经网络 参数估计 气体分馏装置 丙烯塔 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TE96[自动化与计算机技术—控制科学与工程]