Abstract
In this paper, high tracking performance control structure for rigid robot manipulator is proposed. PD-like Sugano type fuzzy system is used as a main controller, while fuzzy-neural network (FNN) is used as a compensator for uncertainties by minimizing suitable function. The output of FNN is added to the reference trajectories to modify input error space, so that the system robust to any change in system parameters. The proposed structure is simulated and compared with computed torque controller. The simulation study has showed the validity of our structure, also showed its superiority to computed torque controller.