本文選題：磁力軸承 + 控制系統(tǒng)　； 參考：《武漢理工大學(xué)》2011年碩士論文

【摘要】：主動(dòng)磁力軸承作為一種優(yōu)秀的機(jī)電綜合體,它具有許多老式的接觸式軸承所不具備的優(yōu)點(diǎn),比如沒有摩擦,故沒有磨損,無需在軸承轉(zhuǎn)子和定子之間涂潤滑劑,因此轉(zhuǎn)子運(yùn)動(dòng)更快,使用壽命更長。正因?yàn)檫@些優(yōu)點(diǎn),主動(dòng)磁力軸承受到工業(yè)領(lǐng)域比如軸承行業(yè)以及學(xué)術(shù)領(lǐng)域的廣泛關(guān)注和熱議。 但由于磁力軸承本身固有的特性,如不穩(wěn)定性、參數(shù)不確定性、模型存在非線性等。在過往的研究中發(fā)現(xiàn),采用傳統(tǒng)的PID控制器無法達(dá)到理想的控制要求。需將新的算法加入其中進(jìn)行分析研究。 本文主要針對磁力軸承中的單自由度磁力軸承進(jìn)行討論分析。在對磁力軸承電磁力和受力問題的分析后,對磁力軸承非線性特性進(jìn)行建模。然后在傳統(tǒng)PID閉環(huán)控制的基礎(chǔ)上,加入BP神經(jīng)網(wǎng)絡(luò)算法和支持向量機(jī)算法對PID的控制參數(shù)進(jìn)行調(diào)整,通過仿真實(shí)驗(yàn)對比兩種算法的控制效果。 文章介紹了神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)規(guī)則,利用神經(jīng)網(wǎng)絡(luò)的高度非線性映射能力,分析設(shè)計(jì)了BP神經(jīng)網(wǎng)絡(luò)PID控制器,仿真結(jié)果表明,BP整定PID控制可以有效地減小超調(diào),增加轉(zhuǎn)子的起浮位置。但因?yàn)樯窠?jīng)網(wǎng)絡(luò)存在局部極小、易出現(xiàn)過擬合等問題,隨著隱含層節(jié)點(diǎn)數(shù)目的增加,控制性能反而變差。 為避免神經(jīng)網(wǎng)絡(luò)的缺點(diǎn),文章提出了基于支持向量機(jī)的磁力軸承PID控制。在對支持向量機(jī)的基本理論及其回歸算法進(jìn)行了詳細(xì)介紹后,首先利用支持向量機(jī)能逼近任意非線性函數(shù)的特點(diǎn),在傳統(tǒng)PID閉環(huán)控制的前提下,對磁力軸承的非線性系統(tǒng)進(jìn)行辨識。然后推導(dǎo)出基于支持向量機(jī)的PID控制器算法,結(jié)合辨識模型,利用Simulink中的M函數(shù)和SVM工具箱實(shí)現(xiàn)基于支持向量機(jī)PID控制的磁力軸承控制系統(tǒng)的仿真實(shí)驗(yàn)。將其與BP神經(jīng)網(wǎng)絡(luò)整定PID控制和傳統(tǒng)PID控制相比較,仿真結(jié)果表明,基于支持向量機(jī)的自適應(yīng)PID控制器的控制效果更好,不僅可以使磁力軸承在更寬范圍內(nèi)起浮,而且調(diào)節(jié)時(shí)間快。
[Abstract]:As an excellent electromechanical complex, active magnetic bearings (AMB) have many advantages that old contact bearings do not have, such as no friction, no wear, no need to apply lubricant between the rotor and stator of the bearing. As a result, the rotor moves faster and has a longer service life. Because of these advantages, active magnetic bearings (AMB) have attracted wide attention and heated discussion in industry such as bearing industry and academic field. However, due to the inherent characteristics of magnetic bearings, such as instability, parameter uncertainty, nonlinear model and so on. In the past research, it is found that the traditional pid controller can not meet the ideal control requirements. The new algorithm should be added to it for analysis and research. In this paper, the single degree of freedom magnetic bearings in magnetic bearings are discussed and analyzed. After analyzing the electromagnetic force and force problem of magnetic bearing, the nonlinear characteristics of magnetic bearing are modeled. Then, on the basis of traditional pid closed-loop control, BP neural network algorithm and support vector machine algorithm are added to adjust the pid control parameters, and the control effects of the two algorithms are compared by simulation experiments. In this paper, the learning rules of neural network are introduced. The BP neural network pid controller is analyzed and designed by using the high nonlinear mapping ability of neural network. The simulation results show that BP tuning pid control can effectively reduce overshoot. Increase the float position of the rotor. However, due to the problem of local minimization and over-fitting of neural networks, the control performance becomes worse with the increase of the number of hidden layer nodes. In order to avoid the shortcoming of neural network, a magnetic bearing pid control based on support vector machine (SVM) is proposed in this paper. After introducing the basic theory of support vector machine and its regression algorithm in detail, firstly, using the feature of support vector function to approximate any nonlinear function, under the premise of traditional pid closed-loop control, The nonlinear system of magnetic bearing is identified. Then, the pid controller algorithm based on support vector machine is deduced, and the simulation experiment of magnetic bearing control system based on support vector machine pid control is realized by using M function and SVM toolbox in Simulink combined with identification model. Compared with BP neural network tuning pid control and traditional pid control, the simulation results show that the adaptive pid controller based on support vector machine has better control effect, and can not only make the magnetic bearing float in a wider range. And the adjustment time is fast.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH133.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 邵傳龍;磁力軸承的模糊控制研究[D];武漢理工大學(xué);2012年

，

本文編號：2079083