Fuzzy Neural Network Control for a Single Flexible-Link Manipulator

Abstract

In this article, a fuzzy neural network controller for a single flexible-link manipulator is considered. A backpropagation neural network operating in the specialized learning mode is employed to decrease the effects of the inherent system nonlinearities, like the motor static friction and the saturation of the electronic amplifier. The neural network output resembles that of a Pulse Width Modulated controller. A fuzzy cell space controller supervises the overall scheme and reduces the amplitude and repetitions of control switchings. The fuzzy controller rules are extracted from a rulebase parameterized in terms of the l₂ and l_∞ norms of the output error. Simulation studies are presented to indicate the effectiveness of the proposed algorithm.