結合基因演算法與T-S模糊方法之感應馬達 速度控制

The Speed Control of Induction Motors Using Integrated T-S Fuzzy Method and Genetic Algorithms

陳文祥(Wen-Hsiang Chen)

指導教授 ： 練光祐

中原大學/工學院/電機工程研究所/碩士(2006年)

https://doi.org/10.6840/CYCU.2006.00392

摘要

摘要

由於交流感應馬達其具有構造較簡單，且維修容易，不會產生火花等優點，更有利用所謂磁場導向控制技術將交流感應馬達解耦，令其如同控制直流馬達簡易。故逐漸取代直流馬達，而廣為工業界所泛用。
在本論文中，我們利用座標係間的轉換關係與定子座標系來做為交流感應馬達的數學模型分析。接著採用適用於非線性系統控制的T-S模糊模式(Takagi-Sugeno fuzzy model)，對上述交流感應馬達作T-S模糊模式化，並利用模糊追蹤控制的理論設計模糊控制器以達到速度控制的目的；而對於該系統的穩定度分析則利用Lyapunov function 予以證明，並利用LMI方法求取系統的控制增益。為了使速度暫態響應更佳，而達到更佳的控制效果。因此我們利用基因演算法（genetic algorithms）對T-S模糊模式中控制迴授增益與歸屬函數之乘積加以調整，與原來設計之T-S模糊器作比較，我們發現該系統的速度暫態響應結果更佳。
最後我們將上述兩種情形控制器予以實作，利用SIMU-DRIVE以及 MATLAB’s Simulink 來實現純T-S模糊控制器及以基因演算法調整T-S模糊器，而實作結果也驗證速度追蹤控制暫態響應的控制效果。

關鍵字：交流感應馬達、T-S模糊模式、模糊追蹤控制、基因演算法、暫態響應、Lyapunov function、LMI。

關鍵字

暫態響應 ； 交流感應馬達 ； 基因演算法 ； 模糊追蹤控制 ； T-S模糊模式

並列摘要

Abstract

The induction motor (IM) has the advantages of being robust, easy to maintain and free of sparks. In addition, many techniques can apply the so-called field oriented control (FOC) to facilitate subsequent design of the high performance speed control for the induction motor in a way similar to that for the DC motor. Therefore, the induction motor has gained its popularity over the DC motor in the industry.
In this thesis, we construct a dynamic model of the induction motor based on the coordinate transformation relation and the stationary frame. The nonlinear dynamic Takagi-Sugeno (T-S) fuzzy model is employed to describe the induction motors, and a fuzzy controller is then designed for speed tracking. The Lyapunov function is used to carry out the stability analysis, and the control gain is obtained by solving a set of linear matrix inequalities (LMIs). To achieve a better speed transient response, we used genetic algorithm to tune the product of the T-S fuzzy controller associated membership function and the control gain. A comparison shows that the GA tuning T-S fuzzy controller yields a better transient response than the T-S fuzzy controller.
Finally, we implemented the two controllers with SIMU-DRIVE and MATLAB’s Simulink. Experimental results demonstrate the effects of speed tracking control.

Keywords: induction motor、T-S fuzzy model、fuzzy tracking control、genetic algorithms、Lyapunov function、LMIs

並列關鍵字

Lyapunov function ； induction motor ； LMIs ； fuzzy tracking control ； genetic algorithms ； T-S fuzzy model

參考文獻

2. L. A Zadeh, “Fuzzy set,” Inform control, vol. 8, pp. 338-352, 1965.

3. E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” inter. J. Man-Mach. Studies, vol. 7, pp. 1 -13, 1975.

4. T. Takagi and M. Sugeno, “Fuzzy identification of system and its application to modeling and control,” IEEE Trans. Systems Man Cybernet. SMC-15, pp. 16-132, Jan./Feb. 1985.

5. H. O. Wang, K. Tanaka, M.F. Griffin “Parallel distributed compensation of nonlinear system by Takagi - Sugeno Fuzzy model,” proceedings of the Fuzzy IEEE/IFES, vol. 95, pp. 531-538.

7. J. H. Holland, adaptation in Natural and Artificial Systems Ann Arbor, MI: Univ. Michigan press, 1975.