An inverse dynamics based fuzzy adaptive state-feedback controller for a nonlinear 3DOF manipulator

This work presents a novel fuzzy adaptive controller for position regulation of the joint variables for a nonlinear 3DOF Revolute-Prismatic-Prismatic (RPP) manipulator. To reach this goal, the forward kinematic equations of the considered series robot are obtained, and the workspace of the end-effector is investigated. Next, the dynamical equations of the robot are found, and a state-feedback controller is designed for stabilizing via the inverse dynamics approach. Through sliding mode surfaces related to the system states, the control parameters are adaptively calculated to enhance the performance of the introduced strategy. Finally, in order to increase the efficiency of the proposed approach, the gains of the designed controller are revealed by employing the fuzzy-logic-based systems. Simulation results for joint variables over time obtained by the suggested technique and recently published methods show the superiority of the fuzzy adaptive control scheme for stabilization of the robot manipulator.