本文選題：橋式起重機(jī)　切入點(diǎn)：小車吊重系統(tǒng)　出處：《北京化工大學(xué)》2011年碩士論文

【摘要】：橋式起重機(jī)作為現(xiàn)代物流裝備之一,廣泛應(yīng)用于各種工業(yè)場(chǎng)合,消除或控制吊重的搖擺對(duì)提高起重機(jī)工作效率和安全性具有重要意義。吊重防搖控制技術(shù)是起重機(jī)作為現(xiàn)代物流裝備所必需具備的功能之一,對(duì)橋式起重機(jī)系統(tǒng)的動(dòng)力學(xué)分析是解決起重機(jī)快速對(duì)位和吊重防遙問(wèn)題的基礎(chǔ)。 本文首先采用拉格朗日方法推導(dǎo)具有普遍意義的橋式起重機(jī)系統(tǒng)的動(dòng)力學(xué)方程——三維、二維和一維橋式起重機(jī)系統(tǒng)的數(shù)學(xué)模型。在合理的范圍內(nèi)對(duì)起重機(jī)系統(tǒng)的非線性動(dòng)力學(xué)方程進(jìn)行簡(jiǎn)化,得到橋式起重機(jī)系統(tǒng)的線性動(dòng)力學(xué)方程——三維、二維和一維橋式起重機(jī)系統(tǒng)的線性模型,為研究橋式起重機(jī)防擺問(wèn)題提供了理論依據(jù)。 針對(duì)吊重?cái)[角等變量現(xiàn)場(chǎng)測(cè)量的難度和成本,利用小車位置信息設(shè)計(jì)全狀態(tài)觀測(cè)器。通過(guò)設(shè)置全狀態(tài)觀測(cè)器重構(gòu)相關(guān)狀態(tài)變量空間,從而將包括小車位置在內(nèi)的所有狀態(tài)變量的估計(jì)信息,提供給防搖控制系統(tǒng)。 論文采用極點(diǎn)配置的狀態(tài)反饋控制方法、線性二次型調(diào)節(jié)器(LQR)最優(yōu)控制及比例積分微分(PID)控制方法,對(duì)起重機(jī)防擺問(wèn)題進(jìn)行仿真研究。仿真結(jié)果表明,上述現(xiàn)代控制方法具有一定的局限性。在對(duì)神經(jīng)網(wǎng)絡(luò)理論進(jìn)行分析研究的基礎(chǔ)上,將徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)的自適應(yīng)PID控制算法應(yīng)用于橋式起重機(jī)吊重防擺系統(tǒng)中。利用二個(gè)RBF神經(jīng)網(wǎng)絡(luò)自適應(yīng)PID控制器對(duì)小車的位置和負(fù)載的擺動(dòng)分別進(jìn)行控制。通過(guò)神經(jīng)網(wǎng)絡(luò)的自適應(yīng)學(xué)習(xí)能力,在線整定PID控制器的比例(P)、積分(I)和微分(D)三個(gè)內(nèi)部參數(shù),實(shí)現(xiàn)具有最佳參數(shù)組合的PID控制。仿真結(jié)果表明,該算法對(duì)起重機(jī)定位無(wú)靜差、無(wú)超調(diào),同時(shí)迅速消除負(fù)載的擺動(dòng)。
[Abstract]:As one of the modern logistics equipment, bridge crane is widely used in various industrial occasions. It is of great significance to eliminate or control the swaying of hoisting load, which is one of the necessary functions of crane as modern logistics equipment, and it is of great significance to improve the efficiency and safety of crane. The dynamic analysis of bridge crane system is the foundation to solve the problem of quick alignment and hoisting remote control. In this paper, the Lagrangian method is used to deduce the dynamic equation of the bridge crane system. The mathematical model of two-dimensional and one-dimensional bridge crane system. The nonlinear dynamic equation of crane system is simplified within a reasonable range, and the linear dynamic equation of bridge crane system is obtained. The linear model of two-dimensional and one-dimensional bridge crane system provides a theoretical basis for studying the anti-swinging problem of bridge crane. In view of the difficulty and cost of the field measurement of swinging angle and other variables, the full state observer is designed by using the vehicle position information, and the relevant state variable space is reconstructed by setting the full state observer. Thus, the estimation information of all state variables, including the position of the car, is provided to the anti-rolling control system. In this paper, the state feedback control method of pole assignment, the optimal control of linear quadratic regulator LQR and the PID-proportional integral differential control method are used to simulate the anti-swinging problem of crane. These modern control methods have some limitations. Based on the analysis of neural network theory, The adaptive PID control algorithm of radial basis function (RBF) neural network is applied to the crane crane anti-swing system. Two RBF neural network adaptive PID controllers are used to control the position and load swing of the trolley respectively. Through the adaptive learning ability of neural networks, In order to realize the PID control with the best combination of parameters, the three internal parameters of the PID controller are adjusted on line. The simulation results show that the algorithm has no static error and no overshoot for the crane positioning, and the load swing is eliminated rapidly at the same time.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH215

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