本文選題：采煤機(jī) + 記憶截割��； 參考：《重慶大學(xué)》2016年碩士論文

【摘要】：采煤機(jī)是綜采工作面的關(guān)鍵設(shè)備之一,是實(shí)現(xiàn)高效集約化采煤、減少井下重大惡性事故和改善工作面勞動(dòng)條件的重要技術(shù)裝備。目前“三機(jī)聯(lián)動(dòng)技術(shù)”已實(shí)現(xiàn)了刮板輸送機(jī)和液壓支架的聯(lián)動(dòng)自動(dòng)化控制,但采煤機(jī)的自動(dòng)化控制還未真正實(shí)現(xiàn)。因此,采煤機(jī)的自動(dòng)化、智能化、無人化控制是實(shí)現(xiàn)整個(gè)工作面自動(dòng)化、智能化和無人化控制的關(guān)鍵,有必要對(duì)采煤機(jī)的自動(dòng)化控制進(jìn)行研究,從而為煤礦的安全、高效生產(chǎn)提供技術(shù)保障。本課題是國(guó)家重點(diǎn)基礎(chǔ)研究計(jì)劃(973計(jì)劃)(2014CB046304)研究?jī)?nèi)容的組成部分,結(jié)合采煤機(jī)記憶截割控制技術(shù),本文進(jìn)行了采煤機(jī)自適應(yīng)截割的控制研究,以實(shí)現(xiàn)采煤機(jī)滾筒高度、牽引速度和滾筒轉(zhuǎn)速的“三調(diào)控”,主要研究?jī)?nèi)容有:(1)在詳細(xì)了解采煤機(jī)整機(jī)以及各部件的結(jié)構(gòu)原理、工作原理和特性的基礎(chǔ)上,建立了基于記憶截割的采煤機(jī)自適應(yīng)截割控制系統(tǒng)的總體架構(gòu),包括系統(tǒng)組成、系統(tǒng)控制原理和“三調(diào)控”耦合關(guān)系,最后分析了記憶截割的原理、記憶策略并建立了采煤機(jī)截割滾筒負(fù)載模型。(2)為了提高采煤機(jī)滾筒調(diào)高系統(tǒng)在調(diào)高過程中的快速性、平穩(wěn)性和準(zhǔn)確性,找到適合井下惡劣工況下滾筒調(diào)高的控制方法,建立了采煤機(jī)滾筒調(diào)高液壓系統(tǒng)的數(shù)學(xué)模型,得到了基于偏差變量的電液比例伺服系統(tǒng)狀態(tài)空間方程,設(shè)計(jì)了變速指數(shù)趨近律的滑模控制器以實(shí)現(xiàn)快、穩(wěn)、準(zhǔn)而抖振小的滾筒調(diào)高過程,有效克服系統(tǒng)的不確定性、外部負(fù)載的擾動(dòng)沖擊。(3)提出了基于記憶截割技術(shù)的平均煤層硬度的預(yù)測(cè)方法,在此基礎(chǔ)上為確定采煤機(jī)記憶跟蹤階段最優(yōu)控制參數(shù)并實(shí)現(xiàn)采煤機(jī)無人自動(dòng)化采煤時(shí)滾筒截割性能綜合最優(yōu),建立了以不同性能指標(biāo)為分目標(biāo)的多目標(biāo)優(yōu)化模型,得到了不同煤層硬度下綜合性能最優(yōu)的牽引速度和滾筒轉(zhuǎn)速,比較了傳統(tǒng)運(yùn)動(dòng)參數(shù)匹配方法和最優(yōu)運(yùn)動(dòng)參數(shù)匹配方法的優(yōu)劣,結(jié)果表明最優(yōu)運(yùn)動(dòng)參數(shù)動(dòng)態(tài)匹配方法的采煤機(jī)滾筒多方面的截割性能都優(yōu)于傳統(tǒng)運(yùn)動(dòng)參數(shù)匹配方法,聯(lián)合調(diào)速優(yōu)于單一牽引調(diào)速。(4)為了實(shí)現(xiàn)采煤機(jī)的“三調(diào)控”自適應(yīng)修正控制以適應(yīng)記憶截割過程中煤巖狀況的變化,將截割電機(jī)電流、牽引電機(jī)電流和調(diào)高油缸油壓信號(hào)進(jìn)行小波包分解處理和數(shù)據(jù)融合,通過建立煤巖截割狀態(tài)的BP神經(jīng)網(wǎng)絡(luò)識(shí)別模型對(duì)煤巖截割狀態(tài)進(jìn)行識(shí)別,在此基礎(chǔ)上結(jié)合截割危險(xiǎn)系數(shù),提出采煤機(jī)“三調(diào)控”的自適應(yīng)修正控制策略,仿真結(jié)果表明自適應(yīng)修正策略可在采煤機(jī)記憶截割過程中適應(yīng)煤巖狀況的變化,保證整機(jī)的安全性和高效性。
[Abstract]:Shearer is one of the key equipments in fully mechanized coal mining face. It is an important technical equipment to realize high efficiency and intensive mining, to reduce serious accidents and to improve working conditions. At present, the "three machine linkage technology" has realized the automatic control of scraper conveyor and hydraulic support, but the automatic control of shearer has not been realized. Therefore, the automatic, intelligent and unmanned control of shearer is the key to realize the automation, intelligence and unmanned control of the whole working face. It is necessary to study the automatic control of the shearer for the safety of coal mine. Efficient production provides technical support. This subject is a part of the research content of the national key basic research plan "Yu973" (2014CB046304). Combined with the memory cutting control technology of shearer, this paper studies the control of self-adaptive cutting of shearer in order to realize the height of shearer drum. On the basis of detailed understanding of the structure, working principle and characteristics of the shearer and its components, the "three regulation" of the traction speed and the rotational speed of the drum is mainly studied. The overall structure of self-adaptive cutting control system of shearer based on memory cutting is established, including system composition, system control principle and coupling relation of "three regulation and control". Finally, the principle of memory cutting is analyzed. In order to improve the speed, stability and accuracy of the height adjustment system of the shearer drum, the control method suitable for the drum height adjustment under the bad working conditions is found, and the load model of the cutting drum of the shearer is established in order to improve the speed, stability and accuracy of the height adjustment system of the drum in the coal mining machine. The mathematical model of hydraulic system for height adjustment of shearer drum is established, the state space equation of electro-hydraulic proportional servo system based on deviation variable is obtained, and a sliding mode controller with variable speed exponential approach law is designed to realize fast and stable. In order to overcome the uncertainty of the system effectively, a prediction method of average seam hardness based on memory cutting technique is proposed for the quasi buffeting drum height adjustment process, which can effectively overcome the uncertainty of the system. On this basis, in order to determine the optimal control parameters in the memory tracking stage of shearer and to realize the comprehensive optimization of the cutting performance of the drum when the shearer is unautomated, a multi-objective optimization model with different performance indexes as the sub-objective is established. The optimal traction speed and drum speed under different coal hardness are obtained, and the advantages and disadvantages of the traditional motion parameter matching method and the optimal motion parameter matching method are compared. The results show that the cutting performance of shearer drum by dynamic matching method of optimal motion parameters is better than that of traditional motion parameter matching method. In order to realize the "three regulation" adaptive correction control of the shearer to adapt to the change of the coal and rock condition during the memory cutting process, the electric current will be cut off. The current of traction motor and the oil pressure signal of the oil cylinder are processed by wavelet packet decomposition and data fusion. Based on the BP neural network recognition model of coal and rock cutting state, the cutting state of coal and rock is identified, and the cutting hazard coefficient is combined. The adaptive correction control strategy of "three regulation" for shearer is put forward. The simulation results show that the adaptive correction strategy can adapt to the change of coal and rock condition in the process of memory cutting of shearer, and ensure the safety and high efficiency of the whole machine.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TD632.1

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