本文選題：無刷直流電機 + 自適應控制　； 參考：《河南科技大學》2014年碩士論文

【摘要】：無刷直流電機以其較小體積，較高功率密度，，結構簡單，以及更好的速度調節(jié)性能等優(yōu)點，在機電能量轉換領域得以廣泛應用。 由于無刷直流電機的多變量，強耦合，非線性等特點，傳統(tǒng)的PID控制方法對于在線尋找適合的PID參數(shù)十分困難，很難達到理想的效果。自適應控制算法對參數(shù)的分辨識別和電機狀態(tài)的預估都是相對于線性模型的，而對于非線性的控制對象，很難滿足控制的精確性和實時性。模糊控制不要求掌握被控對象的精確模型，但是主觀的專家控制規(guī)則無法滿足不同對象的控制要求�？寺∵x擇算法可以有效的搜索到全局最優(yōu)解，并避免陷入局部最優(yōu)解。本文將多種控制策略相互結合，設計了基于多目標克隆選擇算法優(yōu)化的模糊自適應PID控制器。 本文簡述了無刷直流電機的組成結構和運行原理，對無刷直流電機的數(shù)學模型進行了學習，完成了對無刷直流電機的雙閉環(huán)控制系統(tǒng)的建模。系統(tǒng)的外環(huán)轉速調節(jié)模塊利用多目標克隆選擇算法優(yōu)化過的模糊自適應PID控制器的，內環(huán)調節(jié)器則采用傳統(tǒng)的PI控制器。本文提出了利用多目標克隆選擇算法優(yōu)化模糊控制規(guī)則，以及基于精英導向機制的模糊控制兩種方法。這兩種優(yōu)化方法都可以提高無刷直流電機的控制性能：與常規(guī)的控制器相比，系統(tǒng)的響應時間得到了很好的提升，能夠很快的達到穩(wěn)定狀態(tài)，相對于常規(guī)控制器來說具有較高的控制精度。只是多目標克隆選擇算法優(yōu)化模糊自適應PID控制器的控制規(guī)則，雖然能夠獲得控制規(guī)則整體上的最優(yōu)，但無法滿足決策者的偏好，而加入精英導向機制策略的控制器則能夠可根據(jù)決策偏好快速有效地定向搜索Pareto最優(yōu)解。 在Matlab2012/Simulink中搭建了包括電機本體主回路模塊、邏輯換相模塊、速度環(huán)模塊以及電流PI控制器模塊的無刷直流電機雙閉環(huán)控制的仿真模型。仿真結果表明：基于多目標克隆選擇算法優(yōu)化的模糊控制器，能夠系統(tǒng)具有上升時間短，無超調，穩(wěn)態(tài)誤差小等優(yōu)勢，具有較強的魯棒性和自適應性。
[Abstract]:Brushless DC motor (BLDCM) is widely used in the field of electromechanical energy conversion due to its advantages of small volume, high power density, simple structure and better speed regulation performance. Because of the multivariable, strong coupling and nonlinear characteristics of brushless DC motor, the traditional PID control method is very difficult to find suitable PID parameters online, and it is difficult to achieve ideal results. The adaptive control algorithm is relative to the linear model for the identification of parameters and the prediction of motor state, but it is difficult to satisfy the accuracy and real-time performance of the nonlinear control object. Fuzzy control does not require mastering the exact model of the controlled object, but the subjective expert control rules can not meet the control requirements of different objects. The Clone selection algorithm can effectively search the global optimal solution and avoid falling into the local optimal solution. In this paper, a fuzzy adaptive PID controller based on multi-objective clonal selection algorithm is designed by combining various control strategies. In this paper, the composition and operation principle of brushless DC motor are briefly introduced, the mathematical model of brushless DC motor is studied, and the model of double closed loop control system of brushless DC motor is established. The outer loop speed regulation module of the system uses the multi-objective clone selection algorithm to optimize the fuzzy adaptive PID controller, while the inner loop regulator adopts the traditional Pi controller. This paper presents two methods to optimize fuzzy control rules by using multi-objective clonal selection algorithm and two fuzzy control methods based on elitist oriented mechanism. These two optimization methods can improve the control performance of brushless DC motor: compared with the conventional controller, the response time of the system is improved, and the stability can be achieved quickly. Compared with the conventional controller, it has higher control precision. Only the multi-objective clonal selection algorithm can optimize the control rules of fuzzy adaptive PID controller, although it can obtain the overall optimal control rules, but it can not meet the preferences of decision makers. The controller with elitist strategy can search the optimal solution of Pareto quickly and efficiently according to the decision preference. The simulation model of double closed loop control of brushless DC motor including main circuit module logic commutation module speed loop module and current Pi controller module of brushless DC motor is built in Matlab2012/Simulink. The simulation results show that the fuzzy controller based on multi-objective clonal selection algorithm has the advantages of short rise time, no overshoot and small steady-state error, and has strong robustness and adaptability.
【學位授予單位】：河南科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP273.4;TM33

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