Fuzzy adaptive state-feedback control for a revolute-prismatic-revolute robot manipulator

Abstract

In this work, a fuzzy adaptive state-feedback control is designed for the stabilization of a three-degree of freedom revolute-prismatic-revolute (RPR) robot manipulator. At first, the forward kinematic equations are derived and its workspace is investigated. The Lagrange method is applied to find the dynamical equations, and the inverse dynamic approach is utilized to design a state-feedback controller for the regulation of the joint positions. The gradient descent scheme and the sliding surface relations are implemented to adaptively define the control gains. Moreover, a fuzzy system based on the linguistic if-then rules is used to tune the control parameters. The time responses of the joint variables for the introduced scenario are simulated and compared with those of other recently published methods.