Recently, various dynamic control algorithms for industrial manipulators have been proposed. However, computation time, modeling error, and torque-type servo controller design prevented real-time implementation. As a result, most performance evaluations of the dynamic control algorithms were carried out by computer simulation. In this paper, we explore real-time implementation of various dynamic control algorithms, which use different levels of information of the dynamics of a robot system, to show the feasibility and effectiveness of such algorithms. For this purpose, the dynamic equations of a robot manipulator based on Lagrange mechanisms are derived and converted to the equivalent dynamics with respect to the actuator and finally added to the actuator dynamics. Hysteresis current controllers are used as the torque-type servo controller. Experimental results indicate that the computed torque and iterative learning control methods perform better than classical proportional-integral-derivative (PID) control and that these algorithms can be effectively applied to controlling industrial manipulators.