A Novel Structure of Actor-Critic Learning Based on an Interval Type-2 TSK Fuzzy Neural Network

摘要

In this article, a novel structure of actor-critic learning based on an interval type-2 Takagi-Sugeno-Kang fuzzy neural network (AC-IT2-TSK-FNN) is proposed. The proposed structure consists of two IT2-TSK-FNNs that represent the critic and the actor. Structure and parameter learnings are established for all the rules of the proposed structure. The antecedent and consequent parameters for the critic and actor are updated based on the minimization of the proposed cost function. Optimal values for the learning rates are developed and obtained to achieve stability using Lyapunov theory. The obtained results show the superiority of the proposed structure compared to other existing controllers when applied to nonlinear systems.