本文選題：厚度控制　切入點(diǎn)：混沌　出處：《華北理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：板帶鋼的質(zhì)量水平高低在很大程度上取決于熱連軋厚度控制系統(tǒng),隨著國內(nèi)經(jīng)濟(jì)的發(fā)展,鋼材市場中出現(xiàn)了前所未有的激烈競爭,所以更多的企業(yè)注重通過提高產(chǎn)品的質(zhì)量來滿足客戶的需求。但是近年來我國板帶鋼產(chǎn)能出現(xiàn)過剩,再加上板帶鋼產(chǎn)品的厚度不達(dá)標(biāo)等原因出現(xiàn)了很多質(zhì)量問題,這些狀況嚴(yán)重影響了企業(yè)的正常發(fā)展,所以追求更高的厚度精度就成為該領(lǐng)域的關(guān)注焦點(diǎn)。在科技迅速發(fā)展的今天,以傳統(tǒng)控制理論為依據(jù)的帶鋼生產(chǎn)中,有很多困難無法處理,已不能滿足如今高品質(zhì)要求的市場和工業(yè)建筑對鋼材的大量需求,新的控制理論和方法由此誕生,以彌補(bǔ)傳統(tǒng)控制方法的不足,滿足市場的需求。針對標(biāo)準(zhǔn)粒子群算法在尋優(yōu)過程中收斂速度慢、易陷入局部最優(yōu)的缺點(diǎn),課題提出了一種依據(jù)種群早熟收斂程度和個體適應(yīng)值來調(diào)整慣性權(quán)重的混沌粒子群優(yōu)化算法,充分利用混沌優(yōu)化與粒子群優(yōu)化的搜索特征,訓(xùn)練神經(jīng)網(wǎng)絡(luò)的權(quán)值閾值,解決了BP神經(jīng)網(wǎng)絡(luò)收斂速度慢和易陷入局部極值的問題,仿真結(jié)果表明該算法不僅提高了誤差精度,同時也加快了訓(xùn)練收斂的速度。隨后將PID控制規(guī)律融進(jìn)優(yōu)化后的神經(jīng)網(wǎng)絡(luò)中,利用改進(jìn)的混沌粒子群神經(jīng)網(wǎng)絡(luò)整定PID參數(shù)用于厚度控制系統(tǒng),實現(xiàn)神經(jīng)網(wǎng)絡(luò)和PID控制規(guī)律的本質(zhì)結(jié)合,從而構(gòu)造出了一種智能PID控制器,并就系統(tǒng)快速性、抗干擾性和魯棒性與傳統(tǒng)PID控制器進(jìn)行綜合分析比較,仿真結(jié)果表明,前者的性能明顯優(yōu)于后者,改善了傳統(tǒng)PID控制器在人為設(shè)定后就很難滿足時變性要求的缺陷,該控制方法穩(wěn)定可行。
[Abstract]:The quality level of strip steel depends to a great extent on the thickness control system of hot strip rolling. With the development of domestic economy, there has been unprecedented fierce competition in the steel market. Therefore, more enterprises pay attention to meeting the needs of customers by improving the quality of their products. However, in recent years, there has been a lot of quality problems caused by the overcapacity of steel plates and strips in China, and the fact that the thickness of steel products is not up to standard. These conditions have seriously affected the normal development of enterprises, so the pursuit of higher thickness accuracy has become the focus of attention in this field. With the rapid development of science and technology, in the production of strip steel based on traditional control theory, There were many difficulties that could not be dealt with, which could no longer meet the large demand for steel in the market and industrial buildings, which now required high quality, and new control theories and methods were born to make up for the shortcomings of the traditional control methods. To meet the needs of the market, the standard particle swarm optimization algorithm in the process of convergence is slow, easy to fall into local optimum, In this paper, a chaotic particle swarm optimization algorithm, which adjusts inertia weight according to the degree of premature convergence and individual fitness of population, is proposed to train the weight threshold of neural network by making full use of the search features of chaos optimization and particle swarm optimization. The problem of slow convergence speed and easy to fall into local extremum of BP neural network is solved. The simulation results show that the algorithm not only improves the accuracy of error, but also improves the accuracy of BP neural network. At the same time, the speed of training convergence is accelerated. Then the PID control law is incorporated into the optimized neural network, and the improved chaotic particle swarm optimization neural network is used to adjust the PID parameters for the thickness control system. The essential combination of neural network and PID control law is realized, and an intelligent PID controller is constructed. The system's rapidity, anti-jamming and robustness are analyzed and compared with the traditional PID controller. The simulation results show that, The performance of the former is obviously better than that of the latter, which improves the defect that the traditional PID controller is difficult to meet the time-varying requirement after artificial setting, and the control method is stable and feasible.
【學(xué)位授予單位】：華北理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG335.5;TP18

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