A self-organizing neural fuzzy logic controller for turning operations

This paper proposes a neural fuzzy logic controller to achieve self-organizing control for turning operations. A new learning method which is based on a performance index of sliding mode control is used for control rule modifications and some supervision rules are also given to secure rule modifications. One of the major advantages of the proposed model is that it can start from an empty control rule base. Simulation and experimental results of the control of a constant turning force under varying cutting conditions are given to illustrate the effectiveness of the proposed method.