The trajectory tracking control problem for electrically driven redundant robot manipulators is the focus of this research. A unique control technique is given for the electromechanical system by merging the kinematics and dynamics of the manipulator with the actuator dynamics. The controller of this electromechanical system is designed both at the actuator and dynamic levels. The proposed control strategy uses a model-based controller with a model-free radial basis function neural network-based controller with backstepping controller and an adaptive bound to simulate uncertain non-linear mechanical dynamics. A radial basis function neural network is used to estimate the behavior of the unknown electrical dynamics. The designed controller successfully completes both the subtask tracking and the trajectory tracking. In order to produce the required currents and torques, the direct current motors are also controlled by the control system that was created. By applying Lyapunov stability theory, it is demonstrated that the control system is stable and the errors are asymptotically converging. In order to demonstrate the efficacy of the suggested control strategy, simulation results are generated for the rigid link electrically driven redundant robot manipulators.