Different from the schemes examined in previous chapters, this chapter focuses on remote center of motion (RCM) for redundant manipulators. Apart from the operating accuracy, a key issue is the RCM point’s position deviation. Given that an RCM robotic system typically comprises a commercial manipulator and a specialized rod-shaped end-effector, errors in the structural information of the attached end-effector are common. In this chapter, a remote center of cyclic motion (RC \(^2\) M) scheme incorporating data-driven technology is introduced to control manipulators with inaccurate end-effector structural parameters. Meanwhile, a dynamic neural network (DNN) model is constructed to obtain the solution to the scheme. Furthermore, simulations and physical experiments conducted on the Franka manipulator equipped with a rod-shaped end-effector are carried out to validate the effectiveness of the control scheme.