Friction and output backlash compensation of systems using neural network and fuzzy logic

A friction and output backlash compensator is designed for systems by the fuzzy logic (FL) and the neural network (NN). The classification property of FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the friction and output backlash. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the friction and output backlash 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 friction and FL output backlash compensator is simulated on a system to show its efficacy.