本文關(guān)鍵詞： 異步電動機(jī) 隨機(jī)非線性 神經(jīng)網(wǎng)絡(luò) 動態(tài)面控制 輸入飽和　出處：《青島大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：異步電動機(jī)(IM-Induction Motor)憑借低廉的制造成本,簡單的結(jié)構(gòu),高度的可靠性等優(yōu)點在交流調(diào)速系統(tǒng)和傳動系統(tǒng)中發(fā)揮著日益重要的作用。異步電動機(jī)是一個高階、多變量、強(qiáng)耦合的非線性系統(tǒng),而且電機(jī)中阻尼轉(zhuǎn)矩、扭轉(zhuǎn)彈性轉(zhuǎn)矩以及磁飽和等現(xiàn)象會使電機(jī)轉(zhuǎn)矩、自感互感以及繞組電阻等參數(shù)發(fā)生變化,產(chǎn)生隨機(jī)擾動,影響了電機(jī)系統(tǒng)的動態(tài)響應(yīng)速度和控制精度。諸多學(xué)者提出了多種異步電動機(jī)驅(qū)動系統(tǒng)的有效控制方案,但是考慮隨機(jī)擾動的異步電動機(jī)傳動系統(tǒng)的控制策略研究還相對較少。因此,研究適用于異步電動機(jī)隨機(jī)系統(tǒng)的控制方法,提高異步電動機(jī)調(diào)速系統(tǒng)的動靜態(tài)性能具有重要的理論意義和實際應(yīng)用價值。本文結(jié)合動態(tài)面和反步法研究了異步電動機(jī)隨機(jī)系統(tǒng)的神經(jīng)網(wǎng)絡(luò)速度調(diào)節(jié)控制問題。利用徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)來逼近系統(tǒng)中未知的非線性函數(shù),結(jié)合動態(tài)面技術(shù)和反步控制構(gòu)建非線性控制器,有效地消除了隨機(jī)擾動的影響,實現(xiàn)了對異步電動機(jī)調(diào)速系統(tǒng)的高品質(zhì)控制。論文的主要研究成果可以概括如下:1.研究了基于神經(jīng)網(wǎng)絡(luò)的隨機(jī)非線性系統(tǒng)自適應(yīng)動態(tài)面控制問題。使用RBF神經(jīng)網(wǎng)絡(luò)逼近系統(tǒng)的非線性項,引入動態(tài)面通過其一階低通濾波作用避免了導(dǎo)致控制器結(jié)構(gòu)復(fù)雜的“計算爆炸”問題,克服隨機(jī)擾動的影響,根據(jù)反步原理構(gòu)造整個系統(tǒng)的自適應(yīng)控制器。最后由Lyapunov方法分析了該方法的穩(wěn)定性。2.基于動態(tài)面技術(shù)和神經(jīng)網(wǎng)絡(luò)原理,采用自適應(yīng)反步法設(shè)計了異步電動機(jī)隨機(jī)系統(tǒng)的速度調(diào)節(jié)控制策略。利用神經(jīng)網(wǎng)絡(luò)對系統(tǒng)非線性項做逼近處理,動態(tài)面技術(shù)的運用有效避免了傳統(tǒng)反步設(shè)計中普遍存在的“計算爆炸”問題;整個系統(tǒng)的真實控制律在反步控制的最后一步給出,穩(wěn)定性分析表明所構(gòu)造的控制器能夠克服隨機(jī)擾動的不利影響,使系統(tǒng)內(nèi)所有的信號都保持有界。仿真實驗的結(jié)果證明該控制器調(diào)速效果優(yōu)良,且具有較強(qiáng)的魯棒性。3.將輸入飽和限制引入異步電動機(jī)的隨機(jī)系統(tǒng)模型,同樣使用RBF神經(jīng)網(wǎng)絡(luò)處理系統(tǒng)非線性項,動態(tài)面的運用大大減少了控制器構(gòu)造過程中的計算量,彌補(bǔ)了傳統(tǒng)反步設(shè)計的不足。最終構(gòu)建出能使異步電動機(jī)實現(xiàn)良好調(diào)速效果的控制器,克服了隨機(jī)擾動的不利影響。該控制器只有一個自適應(yīng)參數(shù)需要調(diào)節(jié),且考慮了輸入飽和的影響,因此更加具有實際使用價值。
[Abstract]:Induction Motor (IM-Induction Motor) is playing an increasingly important role in AC speed regulation system and transmission system by virtue of its low manufacturing cost, simple structure and high reliability. Strong coupling nonlinear system, and the damping torque, torsional elastic torque and magnetic saturation in the motor will make the motor torque, self-inductance mutual inductance, winding resistance and other parameters change, resulting in random disturbance. The dynamic response speed and control accuracy of motor system are affected. Many scholars have put forward many effective control schemes for asynchronous motor drive system. However, there is relatively little research on the control strategy of the asynchronous motor drive system considering the random disturbance. Therefore, the control method suitable for the asynchronous motor drive system is studied. It has important theoretical significance and practical application value to improve the static and static performance of asynchronous motor speed regulating system. In this paper, the neural network speed regulation control problem of asynchronous motor stochastic system is studied by combining dynamic plane and backstepping method. The radial basis function (RBF) neural network is used to approximate the unknown nonlinear function in the system. Combined with dynamic surface technique and backstepping control, a nonlinear controller is constructed, which effectively eliminates the influence of random disturbance. The main research results of this paper can be summarized as follows: 1. The adaptive dynamic surface control problem of stochastic nonlinear system based on neural network is studied. RBF neural network is used. The nonlinear term of the system of approximating a network, The introduction of dynamic surface through its first-order low-pass filter avoids the problem of "computational explosion", which leads to the complex structure of the controller, and overcomes the influence of random disturbance. The adaptive controller of the whole system is constructed according to the backstepping principle. Finally, the stability of the method is analyzed by the Lyapunov method. 2. Based on the dynamic surface technique and the neural network principle, The adaptive backstepping method is used to design the speed control strategy for the asynchronous motor stochastic system. The nonlinear terms of the system are approximated by the neural network. The application of dynamic surface technology effectively avoids the problem of "computational explosion" in traditional backstepping design, and the real control law of the whole system is given in the last step of backstepping control. The stability analysis shows that the proposed controller can overcome the adverse effects of random disturbances and keep all the signals in the system bounded. The simulation results show that the controller has a good speed regulation effect. And it has strong robustness. 3. The input saturation limit is introduced into the stochastic system model of asynchronous motor. RBF neural network is also used to deal with the nonlinear terms of the system. The application of dynamic surface greatly reduces the calculation in the process of controller construction. It makes up for the shortcoming of the traditional backstepping design. Finally, a controller which can make the asynchronous motor achieve good speed regulation effect is constructed, which overcomes the adverse effect of random disturbance. The controller has only one adaptive parameter to be adjusted. The effect of input saturation is considered, so it has more practical value.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP273;TM343

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