Saturation and deadzone compensation of systems using neural network and fuzzy logic

Abstract

A saturation and deadzone compensator is designed for systems by the fuzzy logic (FL) and the neural network (NN). The classification property of the FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is implemented on a system to show its efficacy.