本文選題：位置隨動(dòng)系統(tǒng)　切入點(diǎn)：負(fù)載擾動(dòng)　出處：《南京理工大學(xué)》2017年碩士論文

【摘要】：隨著國防科技的發(fā)展,對火箭炮武器系統(tǒng)的自動(dòng)化水平和跟蹤精度的要求也越來越高,位置隨動(dòng)系統(tǒng)是組成火箭炮整個(gè)系統(tǒng)的必要環(huán)節(jié),為了得到良好的控制性能,我們必須對位置隨動(dòng)系統(tǒng)提出更高要求�；鸺谠诎l(fā)射時(shí),系統(tǒng)的質(zhì)心位置、剛度、阻尼和轉(zhuǎn)動(dòng)慣量均發(fā)生很大變化,系統(tǒng)參數(shù)具有不確定性,且火箭炮在發(fā)射狀態(tài)時(shí)受連續(xù)燃?xì)饬鳑_擊導(dǎo)致定向器產(chǎn)生振動(dòng),使得后續(xù)發(fā)射在此發(fā)射環(huán)境下命中精度降低。因此,如何克服這些擾動(dòng)及系統(tǒng)不確定性對其影響,提高火箭炮位置隨動(dòng)系統(tǒng)的跟蹤精度和抗干擾能力,是現(xiàn)階段需要研究的問題。本文以某型多管火箭炮的位置隨動(dòng)系統(tǒng)為研究對象,介紹了多管火箭炮的機(jī)械結(jié)構(gòu)以及隨動(dòng)系統(tǒng)的組成及工作原理,建立交流伺服電機(jī)的數(shù)學(xué)模型以及多管火箭炮的交流伺服系統(tǒng)的仿真數(shù)學(xué)模型,分析了位置隨動(dòng)系統(tǒng)的負(fù)載擾動(dòng),為辨識以及控制策略研究奠定了基礎(chǔ)。采取了離線訓(xùn)練與在線調(diào)整的辨識策略。首先采用徑向基函數(shù)(Radial Basis Function,RBF)神經(jīng)網(wǎng)絡(luò)對系統(tǒng)進(jìn)行離線辨識,針對傳統(tǒng)RBF神經(jīng)網(wǎng)絡(luò)參數(shù)確定問題,利用改進(jìn)粒子群算法優(yōu)化RBF神經(jīng)網(wǎng)絡(luò)的中心,寬度及權(quán)值,離線訓(xùn)練得出的參數(shù)作為在線辨識器參數(shù)初始值,避免了振蕩現(xiàn)象,加快了神經(jīng)網(wǎng)絡(luò)的收斂速度。為抑制位置隨動(dòng)系統(tǒng)的負(fù)載擾動(dòng),減少對位置隨動(dòng)系統(tǒng)的干擾。本文設(shè)計(jì)了基于RBF神經(jīng)網(wǎng)絡(luò)的單神經(jīng)元自抗擾控制器,利用自抗擾控制器中的改進(jìn)fal函數(shù)的擴(kuò)張觀測器將系統(tǒng)發(fā)射時(shí)的燃?xì)饬鳑_擊等擾動(dòng)歸于擴(kuò)張狀態(tài)。并考慮到傳統(tǒng)自抗擾控制器的參數(shù)多,難確定的問題,本文利用單神經(jīng)元控制器(Single Neuron Controller,SNC)來代替非線性狀態(tài)誤差反饋器(Nonlinear state error feedback control,NLSEF),其權(quán)值利用 RBF 神經(jīng)網(wǎng)絡(luò)在線辨識器的辨識信息來在線自動(dòng)調(diào)整。結(jié)合國家973項(xiàng)目進(jìn)行了實(shí)驗(yàn),將所設(shè)計(jì)的控制策略在實(shí)驗(yàn)上進(jìn)行驗(yàn)證,實(shí)驗(yàn)結(jié)果表明:該控制策略能夠有效抑制負(fù)載擾動(dòng),具有較強(qiáng)的抗干擾能力。
[Abstract]:With the development of national defense science and technology, the requirement of automatic level and tracking precision of rocket launcher weapon system is more and more high. The position servo system is the necessary link to make up the whole system of rocket launcher, in order to obtain good control performance, We must put forward higher requirements for the position servo system. When launching the rocket launcher, the position of the center of mass, stiffness, damping and moment of inertia of the system all change greatly, and the system parameters are uncertain. Furthermore, the impact of continuous gas flow on the rocket launcher causes the vibration of the directional device, which reduces the accuracy of the subsequent launch in this environment. Therefore, how to overcome these disturbances and the influence of the system uncertainty on it, To improve the tracking accuracy and anti-jamming ability of the position tracking system of rocket launcher is a problem that needs to be studied at present. This paper takes the position servo system of a certain type of multi-barrel rocket launcher as the research object. This paper introduces the mechanical structure of multi-barrel rocket launcher, the composition and working principle of the servo system, establishes the mathematical model of AC servo motor and the simulation mathematical model of AC servo system of multi-barrel rocket launcher. The load disturbance of the position servo system is analyzed, which lays a foundation for the research of the identification and control strategy. The off-line training and on-line adjustment strategy are adopted. Firstly, the radial basis function (RBF) neural network is used to identify the system off-line. Aiming at the problem of parameter determination of traditional RBF neural network, the center, width and weight of RBF neural network are optimized by using improved particle swarm optimization algorithm. The parameters obtained by off-line training are taken as the initial values of parameters of on-line identifiers, and the oscillation phenomenon is avoided. In order to suppress the load disturbance of the position servo system and reduce the interference to the position follow-up system, a single neuron ADRC controller based on the RBF neural network is designed in this paper. The extended observer of the improved fal function in the ADRC is used to return the disturbance such as the gas flow shock to the extended state when the system is launched, and considering the problem that the traditional ADRC has many parameters and is difficult to determine. In this paper, a single Neuron controller (single Neuron controller) is used to replace nonlinear state error feedback control (NLSEFN) of nonlinear state error feedback control (NLSEFN). The weights are automatically adjusted on line by using the identification information of RBF neural network on-line identifiers. The experimental results show that the proposed control strategy can effectively suppress the load disturbance and has strong anti-interference ability.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TJ393;TP183

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