本文選題：模型預(yù)測控制 + 過程監(jiān)測　； 參考：《天津大學(xué)》2016年博士論文

【摘要】：近年來,隨著工業(yè)規(guī)模的不斷擴(kuò)大、復(fù)雜程度的不斷提高以及大量數(shù)據(jù)的涌現(xiàn),工業(yè)過程的質(zhì)量控制和性能控制面臨著巨大的挑戰(zhàn)。預(yù)測和監(jiān)測逐漸成為了兩種必不可少的手段,所以發(fā)展有效的工具實(shí)現(xiàn)對工業(yè)過程的及時(shí)、穩(wěn)定預(yù)測和監(jiān)測,降低各種因素對工業(yè)運(yùn)行性能的負(fù)面影響是具有非常重要意義的。本文的主要工作如下:1.提出了一個(gè)延遲依賴記憶型魯棒模型預(yù)測算法。該系統(tǒng)的時(shí)間延遲雖然大小未知,但具有明確上下界。將最小最大優(yōu)化問題轉(zhuǎn)換為求“在最糟糕狀況下”代價(jià)函數(shù)上界最小問題,利用線性矩陣不等式得到了一個(gè)新的代價(jià)函數(shù)單調(diào)性充分條件;記憶型狀態(tài)反饋控制率被首次引入到魯棒模型預(yù)測控制中,使用所得到的充分條件證明了引入的控制率能使代價(jià)函數(shù)的上界最小,且能保證閉環(huán)系統(tǒng)漸近穩(wěn)定;通過一個(gè)非線性系統(tǒng)的例子說明了所給算法良好的性能。2.提出了一個(gè)新的多尺度非線性過程質(zhì)量監(jiān)測與故障檢測方法,被稱為尺度篩選多尺度算法(Scale-Sifting Multi-Scale Algorithm,SMA)。這個(gè)算法包括尺度篩選基準(zhǔn)、數(shù)據(jù)分解與重構(gòu)以及改進(jìn)的動(dòng)態(tài)核偏最小二乘等三個(gè)部分。與現(xiàn)在流行的多尺度算法相比,尺度篩選多尺度算法的關(guān)鍵特點(diǎn)在于能夠在沒有任何先驗(yàn)假設(shè)的條件下實(shí)現(xiàn)關(guān)鍵尺度數(shù)據(jù)的篩選和重構(gòu),數(shù)據(jù)在沒有任何先驗(yàn)假設(shè)的情況下被分解;尺度篩選基準(zhǔn)被用于篩選出包含過程異常狀況關(guān)鍵特征的關(guān)鍵尺度;根據(jù)所選出的尺度進(jìn)行全局?jǐn)?shù)據(jù)重構(gòu);改進(jìn)的動(dòng)態(tài)核偏最小二乘被用于分析中心化后的重構(gòu)數(shù)據(jù)。仿真和實(shí)驗(yàn)結(jié)果表明尺度篩選多尺度算法在多尺度故障檢測方面優(yōu)越的性能。3.提出了一種新的壓縮稀疏主元分析算法(Compressive Sparse Principal Component Analysis,CSPCA)用于過程監(jiān)測與故障檢測。該方法由壓縮部分重構(gòu)算法以及改進(jìn)的稀疏主元分析算法構(gòu)成。CSPCA算法在沒有任何先驗(yàn)假設(shè)的情況下實(shí)現(xiàn)對于異常信號的壓縮和部分重構(gòu)。根據(jù)主元分析與數(shù)據(jù)矩陣奇異值分解之間的關(guān)系,通過將2,1L范數(shù)作為目標(biāo)函數(shù)和懲罰項(xiàng)得到一個(gè)獲取稀疏主元負(fù)載的凸優(yōu)化問題,并通過一個(gè)迭代算法求解。2,1l懲罰項(xiàng)的引入使得獲得稀疏主元的同時(shí),還能兼顧不大得分主元與小得分主元在監(jiān)測算法中的作用。改進(jìn)的稀疏主元分析算法的單調(diào)性和全局收斂性得到證明。兩個(gè)標(biāo)準(zhǔn)算例的結(jié)果顯示了CSPCA優(yōu)越的性能。
[Abstract]:In recent years, with the continuous expansion of industrial scale, the increasing complexity and the emergence of a large number of data, the quality control and performance control of industrial processes are facing great challenges. Prediction and monitoring have gradually become two indispensable means, so it is of great significance to develop effective tools to realize timely, stable prediction and monitoring of industrial processes, and to reduce the negative effects of various factors on industrial performance. The main work of this paper is as follows: 1. A delay dependent memory robust model prediction algorithm is proposed. The time delay of the system is unknown, but it has definite upper and lower bounds. The minimum maximum optimization problem is transformed into the upper bound minimum problem of the cost function in the worst case, and a new sufficient condition for monotonicity of the cost function is obtained by using the linear matrix inequality (LMI). The memory-type state feedback control rate is introduced into robust model predictive control for the first time. The sufficient conditions obtained prove that the proposed control rate can minimize the upper bound of the cost function and ensure the asymptotic stability of the closed-loop system. An example of a nonlinear system is given to illustrate the good performance of the proposed algorithm. 2. 2. A new multi-scale nonlinear process quality monitoring and fault detection method is proposed, which is called scaling sifting Multi-Scale algorithm. The algorithm consists of three parts: scale screening benchmark, data decomposition and reconstruction, and improved dynamic kernel partial least squares. Compared with the popular multiscale algorithm, the key feature of the scale filtering algorithm is that it can filter and reconstruct the critical scale data without any prior assumptions. The data is decomposed without any prior hypothesis, the scale screening datum is used to screen the key scale which contains the key characteristics of the abnormal state of the process, and the global data is reconstructed according to the selected scale. The improved dynamic kernel partial least squares is used to analyze the reconstructed data after centralization. Simulation and experimental results show that the multi-scale filtering algorithm has excellent performance in multi-scale fault detection. A new compressed sparse principal component analysis (PCA) algorithm, Compressive Sparse Principal Component Analysis (CSPCA), is proposed for process monitoring and fault detection. The proposed method consists of a compression partial reconstruction algorithm and an improved sparse principal component analysis algorithm. The CSPCA algorithm realizes the compression and partial reconstruction of abnormal signals without any prior assumptions. According to the relationship between principal component analysis and singular value decomposition of data matrix, a convex optimization problem for obtaining sparse principal component load is obtained by using 2L norm as objective function and penalty term. By using an iterative algorithm to solve the penalty term of .2ll, the sparse principal component can be obtained, and the function of small score principal component and small score principal component in monitoring algorithm can be taken into account at the same time. The monotonicity and global convergence of the improved sparse principal component analysis algorithm are proved. The results of two standard examples show the superior performance of CSPCA.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP13

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 Naik A;;On the Application of PCA Technique to Fault Diagnosis[J];Tsinghua Science and Technology;2010年02期

，

本文編號：1901995