Self Learning of ANFIS Inverse Control using Iterative Learning Technique

This paper proposes an approach to tune an Adaptive Neuro Fuzzy Inference System (ANFIS) inverse controller using Iterative Learning Control (ILC). The control scheme consists of an ANFIS inverse model and learning control law. Direct ANFIS inverse controller may not guarantee satisfactory response due to different uncertainties associated with operating conditions and noisy training data. In this paper, the ILC makes a class of self tuning to the inputs of ANFIS inverse controller to minimize the overall system error so that the performance iteratively gets improved. The proposed scheme is simple, effective and lays out a unique tuning procedure for designing ANFIS inverse controller through ILC process. General Terms Intelligent Control, Nonlinear Control Systems.