Adaptive control of a stepping motor drive using a hybrid neuro-fuzzy approach

Abstract

Stepping motors are widely used in robotics and in the numerical control of machine tools, where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and a closed-loop control becomes necessary. In this paper, fuzzy logic is applied to control the speed of a stepping motor drive with feedback. A neuro-fuzzy hybrid approach is used to design the fuzzy rule base of the intelligent system for control. In particular, we used the ANFIS (Adaptive Network-based Fuzzy Inference System) methodology to build a Sugeno fuzzy model for controlling the stepping motor drive. An advanced testbed is used in order to evaluate the tracking properties and the robustness capacities of the fuzzy logic controller.