The main innovation of this paper is determining the dynamic load carrying capacity (DLCC) of a flexible joint manipulator (FJM) using a closed form nonlinear optimal control approach. The proposed method is compared with two closed loop nonlinear methods that are usually applied to robotic systems. As a new idea, DLCC of the manipulator is considered as a criterion to compare how well controllers perform point to point mission for the FJMs. The proposed method is compared with feedback linearization (FL) and robust sliding mode control (SMC) methods to show better performance of proposed nonlinear optimal control approach. An optimal controller is designed by solving a nonlinear partial differential equation named the Hamilton–Jacobi–Bellman (HJB) equation. This equation is complicated to solve exactly for complex dynamics so it is solved numerically using an iterative approximation combined with the Galerkin method. In the FL method, angular position, velocity, acceleration and jerk of links are considered as new states to linearize the dynamic equations. In the case of SMC, the dynamic equations of manipulator are changed to the standard form then the Slotine method is used to design the sliding mode controller. Two simulations are performed for a planar and a spatial Puma manipulator and performances of controllers are compared. Finally an experimental test is done on 6R manipulator and the Stereo vision method is used to determine the position and orientation of the end-effector.