| Summary: | 碩士 === 國立中興大學 === 電機工程學系所 === 94 === 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.
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