本文選題：異步電動(dòng)機(jī) + 矢量變頻控制��； 參考：《湖南科技大學(xué)》2014年碩士論文

【摘要】：交流異步電動(dòng)機(jī)以其結(jié)構(gòu)簡單、價(jià)格低廉、堅(jiān)固耐用、并能在惡劣條件具有良好工作狀態(tài)等優(yōu)點(diǎn)被廣泛應(yīng)用于生產(chǎn)、生活的各個(gè)領(lǐng)域，如何提高異步電動(dòng)機(jī)的調(diào)速性能已經(jīng)越來越受到學(xué)者們的關(guān)注。隨著電力電子技術(shù)的快速發(fā)展，使得矢量控制變頻調(diào)速技術(shù)得到了更大的應(yīng)用空間，近年來，大量的智能控制理論也逐漸被運(yùn)用到異步電動(dòng)機(jī)矢量控制變頻調(diào)速系統(tǒng)中。其中，模糊PID控制因其控制效果優(yōu)良且不依賴被控對(duì)象的模型等優(yōu)點(diǎn)，使異步電動(dòng)機(jī)調(diào)速系統(tǒng)的動(dòng)、靜態(tài)性能得到進(jìn)一步提高。但是，想要獲得優(yōu)良的模糊控制器要求設(shè)計(jì)者必須要有足夠的經(jīng)驗(yàn)去選擇隸屬度函數(shù)和控制規(guī)則表，并且在設(shè)定后無法在線更改，使得模糊PID控制存在一定的局限性。針對(duì)上述問題本文將模糊控制器中的隸屬度函數(shù)和控制規(guī)則以十進(jìn)制的方式進(jìn)行編碼，定義兩種不同的粒子分別表示隸屬度函數(shù)和控制規(guī)則，在粒子群算法中進(jìn)行尋優(yōu)，并以ITAE規(guī)則作為適應(yīng)度函數(shù)。完成改進(jìn)PSO算法與模糊PID控制相結(jié)合，實(shí)現(xiàn)模糊控制器的在線整定以獲得更強(qiáng)的適應(yīng)能力。 粒子群算法具有簡單、易于實(shí)現(xiàn)等優(yōu)點(diǎn)在科學(xué)與工程領(lǐng)域得到了很好的驗(yàn)證，但是粒子群優(yōu)化算法與其他進(jìn)化算法一樣存在容易陷入局部最優(yōu)和早熟收斂等缺點(diǎn)。文章分析了其存在缺點(diǎn)的主要原因，并標(biāo)準(zhǔn)粒子群算法的基礎(chǔ)上提出了一種改進(jìn)的粒子群優(yōu)化（DZIA-PSO）算法。首先通過matlab對(duì)算法中粒子的運(yùn)行軌跡進(jìn)行可視化處理，更加直觀的觀察粒子的整個(gè)尋優(yōu)過程，并總結(jié)算法出現(xiàn)早熟現(xiàn)象特點(diǎn)。然后，針對(duì)粒子群局部收斂時(shí)全局最優(yōu)位置的更新停滯的現(xiàn)象，利用sharing函數(shù)對(duì)局部最優(yōu)范圍內(nèi)的粒子進(jìn)行重新初始化并標(biāo)定為死區(qū)，這種方法顯著的提高了粒子群算法在尋優(yōu)過程中的種群多樣性。最后，利用標(biāo)準(zhǔn)測試函數(shù)對(duì)本文的改進(jìn)算法進(jìn)行仿真分析，結(jié)果表明相比于LDW-PSO算法改進(jìn)后的算法具有更好的克服局部最優(yōu)能力。 本文首先介紹了異步電動(dòng)機(jī)的控制原理及數(shù)學(xué)模型，并在此基礎(chǔ)上介紹異步電動(dòng)機(jī)的矢量控制變頻調(diào)速系統(tǒng)。然后，本文完成了基于PSO算法優(yōu)化的模糊PID異步電動(dòng)機(jī)的控制系統(tǒng)設(shè)計(jì)，包括軟件部分和硬件部分，其中硬件設(shè)計(jì)是由以DSP芯片為核心的控制部分和以IPM為核心的驅(qū)動(dòng)部分組成，，軟件算法中包括了磁鏈環(huán)與轉(zhuǎn)矩環(huán)的PI控制算法和速度環(huán)的基于粒子群優(yōu)化的模糊PID控制算法。最后，利用Matlab/Simulink搭建基于粒子群優(yōu)化的模糊PID控制異步電動(dòng)機(jī)調(diào)速系統(tǒng)的仿真模型，在電機(jī)的空載啟動(dòng)、突加負(fù)載以及相同負(fù)載下改變轉(zhuǎn)速等情況進(jìn)行了仿真。仿真實(shí)驗(yàn)驗(yàn)證了相對(duì)常規(guī)的模糊PID控制，基于粒子群優(yōu)化的模糊PID控制在調(diào)速過程中具有響應(yīng)快、超調(diào)低、帶負(fù)載啟動(dòng)能力強(qiáng)等優(yōu)點(diǎn)。
[Abstract]:Ac asynchronous motor is widely used in many fields of production and life because of its simple structure, low price, strong durability, and good working condition in bad conditions. How to improve the speed regulation performance of asynchronous motor has been paid more and more attention by scholars. With the rapid development of power electronics technology, vector control frequency conversion speed control technology has a greater application space, in recent years, a large number of intelligent control theory has been gradually applied to the asynchronous motor vector control frequency conversion speed control system. Among them, the fuzzy PID control has the advantages of excellent control effect and independent of the controlled object model, which makes the dynamic and static performance of the asynchronous motor speed regulation system further improved. However, in order to obtain a good fuzzy controller, the designer must have enough experience to select membership function and control rule table, and can not be changed online after setting, which makes fuzzy PID control have some limitations. In this paper, the membership function and control rule in fuzzy controller are coded in decimal form, and two kinds of particles are defined to represent membership function and control rule respectively, which are optimized in particle swarm optimization algorithm. The ITAE rule is used as fitness function. The improved PSO algorithm is combined with the fuzzy PID control to realize the on-line tuning of the fuzzy controller to obtain better adaptability. Particle swarm optimization (PSO) has many advantages, such as simple and easy to implement, which are well verified in the fields of science and engineering. However, PSO has the same disadvantages as other evolutionary algorithms, such as local optimization and premature convergence. This paper analyzes the main reasons for its shortcomings and proposes an improved particle swarm optimization (PSO) algorithm based on the standard particle swarm optimization (PSO) algorithm. Firstly, the particle trajectory in the algorithm is visualized by matlab, and the whole process of particle optimization is observed more intuitively, and the precocious phenomenon of the algorithm is summarized. Then, aiming at the phenomenon that the global optimal position stagnates when the particle swarm is locally convergent, the particle in the local optimal range is reinitialized by sharing function and demarcated as a dead zone. This method significantly improves the population diversity of PSO in the process of optimization. Finally, the improved algorithm is simulated by the standard test function. The results show that the improved algorithm has better ability to overcome the local optimum than the improved LDW-PSO algorithm. This paper first introduces the control principle and mathematical model of asynchronous motor, and then introduces the vector control variable frequency speed regulating system of asynchronous motor. Then, the design of fuzzy PID asynchronous motor control system based on PSO algorithm is completed, including software and hardware. The hardware design is composed of the control part with DSP chip as the core and the driving part with IPM as the core. The software algorithm includes the Pi control algorithm of magnetic chain loop and torque loop and the fuzzy PID control algorithm based on particle swarm optimization of speed loop. Finally, the simulation model of fuzzy PID control asynchronous motor speed control system based on particle swarm optimization (PSO) is built by using Matlab/Simulink. The simulation is carried out under the condition of no-load starting, sudden loading and changing speed under the same load. The simulation results show that compared with the conventional fuzzy PID control, the fuzzy PID control based on particle swarm optimization (PSO) has the advantages of fast response, overturning, and strong starting ability with load.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM343

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