Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6430
標題: 結合螞蟻群最佳化與模糊分群設計模糊控制器及其FPGA實現
Fuzzy Controller Design by Ant Colony Optimization with Fuzzy Clustering and Its FPGA Implementation
作者: 羅強
Lo, Chiang
關鍵字: fuzzy controller
模糊控制器
ACO algorithm
fuzzy clustering
FPGA
螞蟻最佳化演算法
模糊分群
出版社: 電機工程學系所
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Maniezzo, and A. Colorni, “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Trans. on Systems, man, and cybernetics, Part B: Cybernetics, vol. 26, no. 1, February 1996. [20] M. Dorigo, and L.M. Gambardella, “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem” IEEE Trans. on Evolutionary Computation, vol. 1, no. 1, April 1997. [21] M. Dorigo, and G. Di Caro, “Ant Colony Optimization: A new meta-heuristic” Proc. of the Congress on Evolutionary Computation, vol.2, pp. 1470-1477, July 1999. [22] L. F. Escudero, “An inexact algorithm for the sequential ordering problem,” European Journal of Operations Research, pp. 232-253, 1988. [23] I. Watanabe, and S. Matsui, “Improving the performance of ACO algorithms by adaptive control of candidate set,” Proc. IEEE Int. Conf. IEEE Evolutionary Computation, vol. 2, pp. 1355-1362, 2003. [24] K. M. Sim, and W. H. 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摘要: 本論文提出一新穎的模糊控制器的設計方法。此法結合了螞蟻群最佳化(ACO)演算法與模糊分群法,並簡稱為FC-ACOFC。其目的是同時增進模糊控制器的設計效率以及控制性能表現。FC-ACOFC的架構是利用所提出的新式模糊分群法來產生所需的模糊規則數目及每個維度上的模糊集合。對照傳統的格子狀切割法,FC-ACOFC前件部可以更靈活的分割,並且可以避免模糊集合過度重疊的現象。當一新的模糊規則產生,其後件部值是由ACO從一編列好的候選控制動作中選出。在ACO中,一隻螞蟻的旅程視為從每個模糊規則選到的後件部動作的組合。我們在此提出以所有候選後件部動作來構成費洛蒙陣列與一針對啟發式值更新的即時學習法則。我們利用費洛蒙陣列與啟發式值來尋找所有後件部組合中的最佳組合。為了證明FC-ACOFC的性能,我們模擬了非線性系統控制、水溫控制與渾沌系統控制。對照使用其他演算法來模擬這些問題,我們可以證明FC-ACOFC的表現。 在此所採用的螞蟻群最佳化演算法的硬體實現是使用FPGA晶片。原因是近年來可程式邏輯(PLD)的使用越來越普遍,並且電路設計流程變快且具有彈性。我們應用ACO晶片在模擬水溫控制問題的模糊控制上來證明這晶片的效能。
This thesis proposes a novel design method of Fuzzy Controller by Ant Colony Optimization (ACO) algorithm with Fuzzy Clustering (FC-ACOFC). The objective of FC-ACOFC is to improve both the design efficiency of fuzzy controller and control performance. Structure of FC-ACOFC, including the number of rules and fuzzy sets in each input variable, is created on-line by a newly proposed fuzzy clustering method. In contrast to conventional grid-type partition, the antecedent part of FC-ACOFC is flexibly partitioned, and the phenomenon of highly overlapped fuzzy sets is avoided. Once a new rule is generated, the consequence is selected from a list of candidate control actions by ACO. In ACO, the tour of an ant is regarded as a combination of consequent actions selected from every rule. A pheromone matrix among all candidate consequent actions is constructed and an on-line learning algorithm for heuristic value update is proposed. Searching for the best one among all consequence combinations involves using the pheromone matrix and heuristic values. To verify the performance of FC-ACOFC, simulations on nonlinear system control, water bath temperature control and chaotic system control are performed. Simulations on these problems and comparisons with other algorithms have demonstrated the performance of FC-ACOFC. The ACO used here is hardware implemented on Field Programmable Gate Array (FPGA) chip. The use of Programmable Logic Device (PLD) is more and more general in recent years, and the procedure of circuit deign is fast and elastic. Application of the ACO chip to fuzzy control a simulated water bath temperature control problem has verified the effectiveness of the designed chip.
URI: http://hdl.handle.net/11455/6430
其他識別: U0005-1607200623014500
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1607200623014500
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