A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking

This paper focuses on the design of a recurrent Takagi-Sugeno-Kang interval type-2 fuzzy neural network RTSKIT2FNN for mobile robot trajectory tracking problem. The RTSKIT2FNN is incorporating the recurrent frame of internal-feedback loops into interval type-2 fuzzy neural network which uses simple interval type-2 fuzzy sets in the antecedent part and the Takagi-Sugeno-Kang (TSK) type in the consequent part of the fuzzy rule. The antecedent part forms a local internal feedback loop by feeding the membership function of each node in the fuzzification layer to itself. Initially, the rule base in the RTSKIT2FNN is empty, after that, all rules are generated by online structure learning, and all the parameters of the RTSKIT2FNN are updated online using gradient descent algorithm with varied learning rates VLR. Through experimental results, we demonstrate the effectiveness of the proposed RTSKIT2FNN for mobile robot control.