本文選題：煤泥　切入點(diǎn)：滾筒干燥機(jī)　出處：《昆明理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著煤炭行業(yè)產(chǎn)量和洗選比例的提高,煤泥產(chǎn)量也相應(yīng)地會(huì)增加,要實(shí)現(xiàn)煤泥的大規(guī)模利用,就不得不對(duì)煤泥的含水率以及品質(zhì)提出更高的要求和規(guī)范,除了其它一些辦法,如制水煤漿、摻燒等以外,將煤泥進(jìn)行滾筒干燥后再利用也是一個(gè)值得考慮的方向,這樣不僅在經(jīng)濟(jì)上能取得更高的回報(bào),而且對(duì)于資源的回收利用以及生態(tài)保護(hù)都有著長(zhǎng)遠(yuǎn)的現(xiàn)實(shí)意義和歷史意義。滾筒干燥是目前應(yīng)用最為廣泛的,但是,滾筒干燥的出口溫度控制是一個(gè)非常復(fù)雜的時(shí)變過(guò)程,目前,因?yàn)闈L筒干燥的內(nèi)在結(jié)構(gòu),隨機(jī)擾動(dòng)對(duì)控制系統(tǒng)的影響,導(dǎo)致在理論上并沒(méi)有精確數(shù)學(xué)模型能取得較好控制效果。因此,作為滾筒干燥控制系統(tǒng)中出口溫度的自動(dòng)化控制水平一直得不到明顯的提高。本文主要對(duì)前饋以及前饋-負(fù)反饋的控制原理作了簡(jiǎn)要介紹,分析了RBF神經(jīng)網(wǎng)絡(luò)辯識(shí)的模型參考白適應(yīng)控制,在MATLAB環(huán)境下進(jìn)行仿真,可以看出基于RBF神經(jīng)網(wǎng)絡(luò)辨識(shí)的滾筒干燥機(jī)出口溫度的單神經(jīng)元模型參考自適應(yīng)控制系統(tǒng)對(duì)出口溫度控制模型跟蹤吻合較好,與實(shí)際輸出相差較小且誤差調(diào)整時(shí)間小,系統(tǒng)響應(yīng)時(shí)間短且控制過(guò)程比較穩(wěn)定。通過(guò)飛升曲線法得到了滾筒干燥機(jī)出口溫度的傳遞函數(shù)。設(shè)計(jì)了單神經(jīng)元神經(jīng)網(wǎng)絡(luò)控制器和RBF神經(jīng)網(wǎng)絡(luò)控制器,并將傳統(tǒng)的PID控制和上述兩種控制單獨(dú)進(jìn)行了仿真。并分別兩兩做了比較并進(jìn)行分析,進(jìn)而設(shè)計(jì)了基于RBF神經(jīng)網(wǎng)絡(luò)辨識(shí)的自適應(yīng)控制系統(tǒng),仿真結(jié)果表明,該控制系統(tǒng)在模型的辨識(shí)以及跟蹤方面具有更好的效果,而且還有控制誤差小、魯棒性較好、系統(tǒng)響應(yīng)快等特點(diǎn)。
[Abstract]:With the increase of coal industry output and washing ratio, the output of coal slime will increase accordingly. In order to realize the large-scale utilization of coal slime, it is necessary to put forward higher requirements and norms on the moisture content and quality of coal slime, among other ways, For example, in addition to coal water slurry, mixed burning and so on, reusing the slime after drying the drum is also a direction worthy of consideration, so that it can not only achieve higher economic returns, And it has a long-term practical and historical significance for resource recycling and ecological protection. Drum drying is the most widely used at present, but the outlet temperature control of drum drying is a very complicated and time-varying process. At present, because of the inner structure of drum drying and the influence of random disturbance on the control system, there is no accurate mathematical model to obtain better control effect in theory. As the automatic control level of outlet temperature in drum drying control system, the control principle of feedforward and feed-negative feedback is introduced briefly in this paper. The model reference white adaptive control of RBF neural network identification is analyzed, and the simulation is carried out under the MATLAB environment. It can be seen that the single neuron model reference adaptive control system based on RBF neural network identification for the outlet temperature of the drum dryer is in good agreement with the outlet temperature control model, and the difference with the actual output is small and the error adjustment time is small. The response time of the system is short and the control process is relatively stable. The transfer function of the outlet temperature of the drum dryer is obtained by the flying curve method. A single neuron neural network controller and a RBF neural network controller are designed. The traditional PID control and the above two kinds of control are separately simulated and compared and analyzed, and then the adaptive control system based on RBF neural network identification is designed. The simulation results show that, The control system has better performance in model identification and tracking, and has the advantages of small control error, good robustness and fast response.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TD849

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本文編號(hào)：1590808