基于UKF的未建模過程狀態(tài)估計及其在腈綸聚合中的應(yīng)用
發(fā)布時間：2019-01-05 05:16
【摘要】：對于大多數(shù)復(fù)雜非線性過程,由于很多需要控制的中間變量無法直接測量,這直接影響到過程監(jiān)控系統(tǒng)的實現(xiàn)。非線性濾波技術(shù)為復(fù)雜系統(tǒng)狀態(tài)估計提供了有力的基礎(chǔ),因此得到了廣泛的關(guān)注與研究。目前,不敏卡爾曼濾波Unscented Kalman filter, UKF)算法已經(jīng)成功地解決了很多實際的非線性系統(tǒng)狀態(tài)估計問題。但由于很多不確定因素的存在,使得精確而穩(wěn)定的狀態(tài)估計仍具有較大的挑戰(zhàn)。 論文以腈綸聚合過程作為研究背景,對UKF算法進(jìn)行深入地研究。具體包括以下內(nèi)容： 首先,簡單介紹了二步法腈綸水相聚合工藝過程。對腈綸聚合過程中典型非線性環(huán)節(jié)(連續(xù)攪拌反應(yīng)釜,pH中和過程)的動力學(xué)模型進(jìn)行分析,總結(jié)出復(fù)雜非線性系統(tǒng)的一般特征,為之后濾波算法的改進(jìn)提供了方向。 接著,介紹了解決非線性濾波的典型算法。對UKF算法的具體步驟進(jìn)行分析研究,總結(jié)出UKF算法在濾波過程中面臨的兩大挑戰(zhàn)：系統(tǒng)模型未知和噪聲統(tǒng)計特性未知。 針對UKF要求系統(tǒng)模型精確已知,而實際的非線性系統(tǒng)往往因其復(fù)雜性及不確定性等原因?qū)е孪到y(tǒng)模型難以建立的問題,將UKF和神經(jīng)網(wǎng)絡(luò)結(jié)合(NN-UKF)解決了一類過程模型未知且輸出為狀態(tài)線性組合的非線性過程狀態(tài)估計問題。通過實例的仿真,驗證了NN-UKF算法具有較好的估計效果。將該算法應(yīng)用于腈綸聚合的連續(xù)攪拌反應(yīng)釜過程,解決了連續(xù)攪拌反應(yīng)釜在非線性模型未知時濃度與溫度的估計問題。 針對非線性系統(tǒng)噪聲統(tǒng)計特性難以獲得,而UKF算法對噪聲信息不準(zhǔn)的魯棒性較差導(dǎo)致濾波精度急劇下降,甚至濾波發(fā)散的問題,提出了一種基于Cauchy魯棒函數(shù)的UKF改進(jìn)算法(CR-UKF).以UKF的測量先驗值與其實際值的殘差作為基準(zhǔn),采用聯(lián)合權(quán)函數(shù)對濾波過程的噪聲估計值進(jìn)行實時修正,降低噪聲不準(zhǔn)的估計值的權(quán)重,提高了UKF算法的精度。兩個實例的仿真結(jié)果表明,CR-UKF算法對提高噪聲估計不準(zhǔn)時UKF的狀態(tài)估計精度非常有效。將CR-UKF算法應(yīng)用于腈綸聚合過程的pH中和過程,一方面提高了中和反應(yīng)pH值的監(jiān)控精度,另一方面提高了離子濃度的估計精度。 最后,對本文所提出的改進(jìn)算法進(jìn)行總結(jié),并對之后的工作做了進(jìn)一步的展望。
[Abstract]:For most complex nonlinear processes, many intermediate variables that need to be controlled can not be measured directly, which directly affects the realization of process monitoring system. Nonlinear filtering technology provides a powerful basis for state estimation of complex systems, so it has been widely studied. At present, the nonsensitive Kalman filter (Unscented Kalman filter, UKF) algorithm has successfully solved many practical nonlinear system state estimation problems. However, due to the existence of many uncertain factors, accurate and stable state estimation still poses a great challenge. In this paper, the polymerization process of acrylic fiber is used as the research background, and the UKF algorithm is deeply studied. The main contents are as follows: firstly, the water phase polymerization process of two-step acrylic fiber is briefly introduced. The dynamic models of typical nonlinear links (continuous stirred tank reactor, pH neutralization process) in the polymerization process of acrylic fiber were analyzed, and the general characteristics of the complex nonlinear system were summarized, which provided a direction for the improvement of filtering algorithm. Then, the typical algorithms to solve nonlinear filtering are introduced. The concrete steps of UKF algorithm are analyzed and studied, and two major challenges in filtering process of UKF algorithm are summarized: unknown system model and unknown statistical characteristics of noise. In view of UKF, the system model is required to be accurately known, but the actual nonlinear system is often difficult to establish because of its complexity and uncertainty. Combining UKF with neural network (NN-UKF), the problem of state estimation for a class of nonlinear processes with unknown process model and linear state combination is solved. The simulation results show that the NN-UKF algorithm has a good estimation effect. The algorithm is applied to the process of acrylonitrile polymerization and the estimation of concentration and temperature of continuous stirred tank reactor is solved when the nonlinear model is unknown. It is difficult to obtain the statistical characteristics of noise in nonlinear systems, but the poor robustness of UKF algorithm to noise information leads to a sharp decline in filtering accuracy and even filtering divergence. An improved UKF algorithm (CR-UKF) based on Cauchy robust function is proposed. Based on the residual error between the measured prior value and the actual value of UKF, the joint weight function is used to modify the noise estimation value in the filtering process in real time, which reduces the weight of the noise estimation value and improves the accuracy of the UKF algorithm. The simulation results of two examples show that the CR-UKF algorithm is very effective to improve the accuracy of the state estimation of the noise estimation unpunctual UKF. The CR-UKF algorithm is applied to the pH neutralization process of acrylic polymerization. On the one hand, the monitoring accuracy of the neutralization reaction pH value is improved; on the other hand, the accuracy of ion concentration estimation is improved. Finally, the improved algorithm proposed in this paper is summarized, and the future work is prospected.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TQ342.31;TN713
本文編號：2401321
[Abstract]:For most complex nonlinear processes, many intermediate variables that need to be controlled can not be measured directly, which directly affects the realization of process monitoring system. Nonlinear filtering technology provides a powerful basis for state estimation of complex systems, so it has been widely studied. At present, the nonsensitive Kalman filter (Unscented Kalman filter, UKF) algorithm has successfully solved many practical nonlinear system state estimation problems. However, due to the existence of many uncertain factors, accurate and stable state estimation still poses a great challenge. In this paper, the polymerization process of acrylic fiber is used as the research background, and the UKF algorithm is deeply studied. The main contents are as follows: firstly, the water phase polymerization process of two-step acrylic fiber is briefly introduced. The dynamic models of typical nonlinear links (continuous stirred tank reactor, pH neutralization process) in the polymerization process of acrylic fiber were analyzed, and the general characteristics of the complex nonlinear system were summarized, which provided a direction for the improvement of filtering algorithm. Then, the typical algorithms to solve nonlinear filtering are introduced. The concrete steps of UKF algorithm are analyzed and studied, and two major challenges in filtering process of UKF algorithm are summarized: unknown system model and unknown statistical characteristics of noise. In view of UKF, the system model is required to be accurately known, but the actual nonlinear system is often difficult to establish because of its complexity and uncertainty. Combining UKF with neural network (NN-UKF), the problem of state estimation for a class of nonlinear processes with unknown process model and linear state combination is solved. The simulation results show that the NN-UKF algorithm has a good estimation effect. The algorithm is applied to the process of acrylonitrile polymerization and the estimation of concentration and temperature of continuous stirred tank reactor is solved when the nonlinear model is unknown. It is difficult to obtain the statistical characteristics of noise in nonlinear systems, but the poor robustness of UKF algorithm to noise information leads to a sharp decline in filtering accuracy and even filtering divergence. An improved UKF algorithm (CR-UKF) based on Cauchy robust function is proposed. Based on the residual error between the measured prior value and the actual value of UKF, the joint weight function is used to modify the noise estimation value in the filtering process in real time, which reduces the weight of the noise estimation value and improves the accuracy of the UKF algorithm. The simulation results of two examples show that the CR-UKF algorithm is very effective to improve the accuracy of the state estimation of the noise estimation unpunctual UKF. The CR-UKF algorithm is applied to the pH neutralization process of acrylic polymerization. On the one hand, the monitoring accuracy of the neutralization reaction pH value is improved; on the other hand, the accuracy of ion concentration estimation is improved. Finally, the improved algorithm proposed in this paper is summarized, and the future work is prospected.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TQ342.31;TN713
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 王慶超;于占東;;基于反步法的CSTR神經(jīng)網(wǎng)絡(luò)非奇異自適應(yīng)控制[J];吉林大學(xué)學(xué)報(信息科學(xué)版);2006年01期
2 周本川;程向紅;;魯棒UKF濾波算法在SINS初始對準(zhǔn)中的應(yīng)用(英文)[J];Journal of Southeast University(English Edition);2011年01期
3 洪璋傳;丙烯腈連續(xù)水相聚合的過程分析及控制[J];合成纖維工業(yè);1994年02期
4 楊慧中,陶振麟,張素貞,薛正娟,黃翔宇,顧文蘭,張超峰;聚丙烯腈生產(chǎn)中pH值和H_2SO_3濃度的軟測量模型[J];合成纖維;2002年01期
5 李伯耿,趙建青,袁惠根,潘祖仁;丙烯腈連續(xù)水相聚合宏觀動力學(xué)模型[J];化工學(xué)報;1991年02期
6 周凌柯;李九龍;薄煜明;;基于一種新的魯棒目標(biāo)函數(shù)的化工過程數(shù)據(jù)校正[J];化工自動化及儀表;2012年03期
7 ;Unscented Kalman filtering in the additive noise case[J];Science China(Technological Sciences);2010年04期
8 宋崎,韓建達(dá);基于UKF的移動機(jī)器人主動建模及模型自適應(yīng)控制方法[J];機(jī)器人;2005年03期
9 姜春陽;邱彤;陳丙珍;趙勁松;;一種改進(jìn)的基于魯棒估計的動態(tài)數(shù)據(jù)校正方法[J];計算機(jī)與應(yīng)用化學(xué);2007年10期
10 潘泉,楊峰,葉亮,梁彥,程詠梅;一類非線性濾波器——UKF綜述[J];控制與決策;2005年05期
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