Self Learning of ANFIS Inverse Control using Iterative Learning Technique

Abstract

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.

Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

“…In reference [2], the direct model reference adaptive control is designed for coupled tank system. While, an iterative learning control is used to tune an adaptive neurofuzzy inference system inverse controller for tank system [3]. Khan and Spurgeon presented a second order sliding mode control algorithm for a class of MIMO nonlinear system.…”