本文選題：感應(yīng)電動(dòng)機(jī)　切入點(diǎn)：命令濾波技術(shù)　出處：《青島大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：感應(yīng)電動(dòng)機(jī)以其簡(jiǎn)單的結(jié)構(gòu),低廉的制造成本,運(yùn)行時(shí)較低的損耗和較高的穩(wěn)定性等優(yōu)勢(shì)使其逐漸在工業(yè)應(yīng)用中扮演了重要角色。然而,由于感應(yīng)電機(jī)驅(qū)動(dòng)系統(tǒng)為高階、強(qiáng)耦合、參數(shù)時(shí)變的非線性系統(tǒng),使其在運(yùn)行過程中容易受負(fù)載擾動(dòng)和參數(shù)時(shí)變的影響而降低運(yùn)行效率。所以,研究新穎有效的控制方法來(lái)提升感應(yīng)電機(jī)驅(qū)動(dòng)系統(tǒng)的動(dòng)態(tài)和靜態(tài)特性,是一個(gè)具有重要理論意義與實(shí)際生產(chǎn)價(jià)值的研究方向。針對(duì)經(jīng)典的感應(yīng)電機(jī)控制策略中存在的一些不足,本文結(jié)合命令濾波自適應(yīng)反步原理研究了感應(yīng)電機(jī)的神經(jīng)網(wǎng)絡(luò)速度調(diào)節(jié)和位置跟蹤控制方法。運(yùn)用神經(jīng)網(wǎng)絡(luò)逼近感應(yīng)電機(jī)系統(tǒng)中的非線性項(xiàng),通過引入命令濾波技術(shù)和自適應(yīng)反步原理,實(shí)現(xiàn)了對(duì)感應(yīng)電機(jī)驅(qū)動(dòng)系統(tǒng)的有效控制。論文的主要研究成果如下:1.研究了具有嚴(yán)格反饋結(jié)構(gòu)的非線性系統(tǒng)模型的命令濾波自適應(yīng)反步控制策略。利用神經(jīng)網(wǎng)絡(luò)系統(tǒng)逼近系統(tǒng)中的非線性項(xiàng),通過引入命令濾波器有效地避免了“計(jì)算爆炸”問題,根據(jù)反步原理構(gòu)造RBF網(wǎng)絡(luò)自適應(yīng)控制器,并使用李雅普諾夫方法給出了系統(tǒng)的穩(wěn)定性證明。2.研究了基于命令濾波神經(jīng)網(wǎng)絡(luò)自適應(yīng)反步技術(shù)的感應(yīng)電動(dòng)機(jī)速度調(diào)節(jié)和位置跟蹤控制問題。利用神經(jīng)網(wǎng)絡(luò)系統(tǒng)逼近感應(yīng)電動(dòng)機(jī)系統(tǒng)中的非線性項(xiàng),通過引入命令濾波技術(shù)來(lái)避免對(duì)虛擬控制信號(hào)的反復(fù)求導(dǎo)問題,采用反步法構(gòu)造整個(gè)系統(tǒng)模型的真實(shí)控制器,根據(jù)李雅普諾夫原理對(duì)其穩(wěn)定性進(jìn)行分析。本文構(gòu)造出的真實(shí)控制律只有一個(gè)自適應(yīng)參數(shù),因此更容易應(yīng)用于工程實(shí)際,并且能夠克服參數(shù)不確定及負(fù)載擾動(dòng)的影響,模型中的每個(gè)狀態(tài)信號(hào)都是有界的。最后在MATLAB仿真環(huán)境下,對(duì)所提策略的有效性給出了直觀證明。3.研究了基于濾波誤差補(bǔ)償機(jī)制的命令濾波自適應(yīng)神經(jīng)網(wǎng)絡(luò)速度調(diào)節(jié)控制器。利用RBF網(wǎng)絡(luò)逼近感應(yīng)電機(jī)驅(qū)動(dòng)模型中的非線性項(xiàng),命令濾波器被用來(lái)解決由于對(duì)虛擬控制律反復(fù)求取導(dǎo)數(shù)而大大增加計(jì)算負(fù)擔(dān)的不足,通過引入誤差補(bǔ)償信號(hào),對(duì)命令濾波器的濾波誤差進(jìn)行有效補(bǔ)償,從而減少了濾波器誤差對(duì)控制系統(tǒng)產(chǎn)生的影響,同時(shí)運(yùn)用反步原理構(gòu)造整個(gè)系統(tǒng)的真實(shí)控制器。所構(gòu)造的控制器可以使感應(yīng)電機(jī)的轉(zhuǎn)子速度快速準(zhǔn)確地對(duì)期望信號(hào)進(jìn)行有效跟蹤,且所有狀態(tài)變量均有界。從仿真的結(jié)果可以看出,在參數(shù)未知和負(fù)載擾動(dòng)的影響下,所提出的控制方法仍能使系統(tǒng)獲得很好的魯棒性和跟蹤效果。
[Abstract]:Induction motors have gradually played an important role in industrial applications because of their simple structure, low manufacturing cost, low loss while running and high stability. However, the induction motor drive system is of high order. The nonlinear system with strong coupling and time-varying parameters makes it easy to be affected by load disturbance and parameter time-varying during operation. To study new and effective control methods to improve the dynamic and static characteristics of induction motor drive system is a research direction with important theoretical significance and practical production value. In this paper, the neural network speed regulation and position tracking control method of induction motor are studied based on the principle of command filter adaptive backstepping. The nonlinear term in induction motor system is approximated by neural network. By introducing command filtering technique and adaptive backstepping principle, The main research results of this paper are as follows: 1. The command filtering adaptive backstepping control strategy of nonlinear system model with strict feedback structure is studied. The system approximates the nonlinear term in the system, By introducing a command filter, the problem of "computational explosion" is effectively avoided, and the adaptive controller of RBF network is constructed according to the principle of backstepping. The stability proof of the system is given by using Lyapunov method. Secondly, the speed regulation and position tracking control of induction motor based on command filtering neural network adaptive backstepping technique are studied. Approaching the nonlinear term in Induction Motor system, The command filtering technique is introduced to avoid the repeated derivation of the virtual control signal, and the real controller of the whole system model is constructed by using the backstepping method. The stability of the real control law is analyzed according to Lyapunov principle. The real control law constructed in this paper has only one adaptive parameter, so it is easier to be applied to engineering practice and can overcome the influence of parameter uncertainty and load disturbance. Each state signal in the model is bounded. Finally, in the MATLAB simulation environment, The effectiveness of the proposed strategy is proved intuitively. 3. The command filter adaptive neural network speed regulator based on filter error compensation mechanism is studied. The nonlinear term in the driving model of induction motor is approximated by RBF neural network. The command filter is used to solve the problem that the derivative of the virtual control law is repeatedly obtained and the computational burden is greatly increased. By introducing the error compensation signal, the filter error of the command filter can be compensated effectively. Thus reducing the influence of filter error on the control system, At the same time, the real controller of the whole system is constructed by using the principle of backstepping. The controller can make the rotor speed of induction motor track the desired signal efficiently and accurately. And all state variables are bounded. The simulation results show that the proposed control method can still achieve good robustness and tracking performance under the influence of unknown parameters and load disturbance.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273;TM346

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