| Summary: | 碩士 === 國立中興大學 === 電機工程學系 === 93 === This thesis proposes the application of Ant Colony Optimization (ACO) algorithm to design the consequent parts of a fuzzy controller. This is called ACO-FC. The ACO-FC that is improved design efficiency and control performance of main objectives. For a fuzzy controller, we partition the antecedent part in grid-type, and then list all candidate consequent values of the rules. The path of an ant is regarded as one combination of consequent values selected from every rule. Searching of the best one among all combinations is based on thickness of the pheromone of ACO. Performance of the proposed method has been shown to be better than genetic algorithm on simulations of cart-pole balancing and temperature control problems.
The used ACO is hardware-implemented on FPGA (Field Programmable Gate Array) chip. The implemented chip contains one memory unit for depositing thickness of pheromone, one random number generator of 16 bits, one 16 bits divider, and some other logic operation units. To verify the performance of the chip, we have applied it on simulation of water bath temperature control.
For reinforcement fuzzy controller design problem, we propose the incorporation of Fuzzy-Q learning into ACO, called FQ-ACO, to further improve the performance of ACO. For all the candidates in the consequent part of a rule, we assign each one a corresponding Q-value. Update of the Q-value is based on fuzzy-Q learning. The best combination of consequent values of a fuzzy controller is searched according to both pheromone and Q-value. To verify the performance of FQ-ACO, reinforcement fuzzy control of water bath temperature control system, magnetic levitation control system, and truck back-upper control are simulated. Simulations on the three problems with ACO alone and fuzzy-Q alone are also performed, respectively. Performance of FQ-ACO is verified from the comparisons.
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