A New Type-II Fuzzy System for Flexible-Joint Robot Arm Control

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on the Interval Gaussian Type-II Fuzzy sets in the antecedent part and Gaussian Type-I Fuzzy sets as coefficients of a linear combination of the input variables in the consequent part is presented. The capability of the proposed control method to function approximation and dynamical system identification is investigated. An adaptive learning rate based on the Backpropagation method with guaranteed convergence is employed for parameter learning. Finally, the proposed method is applied to control a flexible-joint robot arm. The simulation results show the robustness and effectiveness of the proposed control method. The proposed control method is also compared with the conventional ANFIS method.