Training of fuzzy inference systems by combining variable structure systems technique and Levenberg-Marquardt algorithm

Abstract

This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg-Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the parameter update rule in continuous time and application of sliding control method to the gradient based training procedure. In this paper, a fuzzy inference mechanism that can be trained such that the adjustable parameter values are forced to settle down (parameter stabilization) while minimizing an appropriate cost function (cost optimization), is discussed. In the application example, control of a two degrees of freedom direct drive SCARA robotic manipulator is considered. As the controller, a standard fuzzy system architecture is used and the parameter tuning is performed by the proposed algorithm.