This study proposes a lifelong deep learning control scheme for robotic manipulators with bounded disturbances. This scheme involves the use of an online tunable deep neural network (DNN) to approximate the unknown nonlinear dynamics of the robot. The control scheme is developed by using a singular value decomposition-based direct tracking error-driven approach, which is utilized to derive the weight update laws for the DNN. To avoid catastrophic forgetting in multi-task scenarios and to ensure lifelong learning (LL), a novel online LL scheme based on elastic weight consolidation is included in the DNN weight-tuning laws. Our results demonstrate that the resulting closed-loop system is uniformly ultimately bounded while the forgetting is reduced. To demonstrate the effectiveness of our approach, we provide simulation results comparing it with the conventional single-layer NN approach and confirm its theoretical claims. The cumulative effect of the error and control input in the multitasking system shows a 43% improvement in performance by using the proposed LL-based DNN control over recent literature.