As a widespread and fundamental element in robotic systems, the redundant manipulator plays a crucial role in automated production processes. The motion control of a redundant manipulator is a key issue that requires careful resolution. Therefore, this chapter investigates a dynamic neural network (DNN) model designed for the double-index (DI) scheme to manage the cyclic motion control of redundant manipulators. In the DI scheme, the optimization indexes are set as the minimum kinetic energy (MKE) and minimum joint-angle offset (MJAO), which also incorporates the manipulators’ kinematic equations and joint limit constraints. Subsequently, the DI scheme is transformed into a quadratic programming (QP) problem. Besides, the DNN model based on the iteration of related parameters is derived to solve the QP problem, through which joint data (i.e., joint angle, joint velocity, and joint acceleration) are obtained to drive the motion of the redundant manipulator. To evaluate the DI scheme’s performance under the DNN-based control, comparative simulations are conducted between the presented scheme and the minimum acceleration norm (MAN) scheme.