A New Method of Variable Universe Fuzzy Control Based on Q Learning Algorithm

When the control function of a variable universe fuzzy controller is transmitted to the offspring, there are usually some ´distortions´ which lead to the error of the algorithms. To solve this problem, this paper proposes a novel optimal method of variable universe fuzzy control based on Q learning algorithm. This algorithm gives an idea of adjusting universes by contraction-expansion factors and geometric proportional factor, and then optimizing the parameters through Q learning algorithm to minimize the performance indexes of the controller for the purpose of reducing the ´distortion rate´ in the control process, and improving control performance. Finally, this paper applies the algorithm to non-minimum phase system. Results indicate that this algorithm not only has good robustness and dynamic performance but also has better control performance than the variable universe fuzzy controller.