Ant Colony Optimization Algorithm for Fuzzy Controller Design and Its FPGA Implementation

An ant colony optimization (ACO) application to a fuzzy controller (FC) design, called ACO-FC, is proposed in this paper for improving design efficiency and control performance, as well as ACO hardware implementation. An FC's antecedent part, i.e., the ldquoifrdquo part of its composing fuzzy if-then rules, is partitioned in grid-type, and all candidate rule consequent values are then listed. An ant trip is regarded as a combination of consequent values selected from every rule. A pheromone matrix among all candidate consequent values is constructed. Searching for the best one among all combinations of rule consequent values is based mainly on the pheromone matrix. The proposed ACO-FC performance is shown to be better than other metaheuristic design methods on simulation examples. The ACO used in ACO-FC is based on the known ant colony system and is hardware implemented on a field-programmable gate array chip. The ACO chip application to fuzzy control of a simulated water bath temperature control problem has verified the designed chip effectiveness.