【摘要】：隨著工業(yè)過(guò)程的日益復(fù)雜,控制系統(tǒng)往往具有多變量、非線性、工況范圍廣等特點(diǎn)。為提升復(fù)雜動(dòng)態(tài)系統(tǒng)的控制性能,基于多模型的非線性系統(tǒng)辨識(shí)與控制方法愈發(fā)受到關(guān)注。本文面向非線性動(dòng)態(tài)系統(tǒng),分別提出了在單維調(diào)度變量及多維調(diào)度變量情況下基于最優(yōu)邊界劃分的辨識(shí)方法,通過(guò)調(diào)節(jié)子模型邊界參數(shù)優(yōu)化多模型輸出誤差,突出了樣本點(diǎn)與子模型之間的對(duì)應(yīng)關(guān)系,并從模型精度和控制性能兩方面說(shuō)明該辨識(shí)模型的優(yōu)點(diǎn)。本文的主要貢獻(xiàn)如下:1)對(duì)于多模型的調(diào)度變量為一維的情況,提出一種使輸出誤差最小的最優(yōu)邊界劃分辨識(shí)方法。該方法使用基于局部模型參數(shù)向量的聚類方法初始化劃分?jǐn)?shù)據(jù)集,充分考慮了多模型中子模型邊界對(duì)模型精度帶來(lái)的影響,在精準(zhǔn)劃分?jǐn)?shù)據(jù)的基礎(chǔ)上辨識(shí)子模型參數(shù)。通過(guò)與一般的聚類方法和基于工作點(diǎn)線性化的模型相對(duì)比,在同樣的調(diào)度變量下,該方法有效地提升了模型精度。2)對(duì)于調(diào)度變量為多維的情況,基于Softmax分類方法,提出一種使輸出誤差最小的最優(yōu)邊界劃分辨識(shí)方法。該方法解決了多維調(diào)度變量下子模型邊界不易于初始化表達(dá)和子模型區(qū)域不完全劃分的兩個(gè)難題,使基于最優(yōu)邊界劃分的辨識(shí)方法在調(diào)度變量為多維的情況下得到推廣。3)基于辨識(shí)得到的PWA模型,設(shè)計(jì)了基于MLD框架的多模型預(yù)測(cè)控制器。通過(guò)對(duì)比不同辨識(shí)模型在預(yù)測(cè)控制器下的控制效果,進(jìn)一步驗(yàn)證本文所提出辨識(shí)方法在控制性能上的優(yōu)勢(shì)。
[Abstract]:With the increasing complexity of industrial processes, control systems are often multivariable, nonlinear, and have a wide range of operating conditions. In order to improve the control performance of complex dynamic systems, the identification and control methods of nonlinear systems based on multiple models have attracted more and more attention. In this paper, for nonlinear dynamic systems, an identification method based on optimal boundary partition is proposed in the case of single dimensional scheduling variables and multidimensional scheduling variables, and the output errors of multiple models are optimized by adjusting the boundary parameters of submodels. The corresponding relationship between the sample points and the sub-model is highlighted, and the advantages of the identification model are illustrated in terms of model precision and control performance. The main contribution of this paper is as follows: (1) in the case that the scheduling variable of multiple models is one-dimensional, an optimal boundary partition identification method is proposed to minimize the output error. In this method, the partitioned data set is initialized by clustering method based on local model parameter vector. The influence of the boundary of multi-model neutron model on model accuracy is fully considered, and the submodel parameters are identified on the basis of accurate partitioning data. Compared with the general clustering method and the model based on working-point linearization, under the same scheduling variables, this method effectively improves the precision of the model .2) for the multi-dimensional scheduling variables, based on the Softmax classification method, An optimal boundary partition identification method with minimum output error is proposed. This method solves the two difficult problems of multi-dimensional scheduling variable model boundary is not easy to initialize the representation and sub-model area is not completely partitioned. The identification method based on optimal boundary partition is extended to 3. 3) based on the identified PWA model, a multi model predictive controller based on MLD framework is designed. By comparing the control effect of different identification models under predictive controller, the superiority of the proposed identification method in control performance is further verified.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273

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本文編號(hào)：2148119