Fuzzy Type-2 Controllers, Optimization, Ant Colony Optimization, Genetic Algoritm, Particle Swarm Optimization. 

Differents stategies and control algorithms are already tested and registered by indus- try. Among the existing techniques, fuzzy controllers stand out for their ability to deal with nonlinearities present in real plants. Another fuzzy also allows to best represent ex- pert knowledge, which is mathematically inaccurate. This qualification proposal studied the two types of fuzzy controllers, based on Sugeno Model, the fuzzy type-1 is classified as conventional fuzzy and fuzzy type-2. In this study is used optimization techniques seeking to tune controllers in order to solve one of the biggest problem in fuzzy logic, its tunning. Ant colony, particle swarm and genetic algorithm are used and evaluated to this problem. A servo motor-dc is used to validate fuzzys controllers and pi controller obtained by optimization tecniques. In order to quantify and qualify each controller, three indices were used IEA, ITEA and Goodhart index. The results obtained prove that the type-2 fuzzy controller presented significant gain for the control of this plant, when optimized with the PSO method. From the results, it can also be inferred that the ant algorithm was not adequate for this problem, with the proposed evaluation function.