In this paper, we present a lifelong deep learning-based control of robotic manipulators with nonstandard adaptive laws using singular value decomposition (SVD) based direct tracking error driven (DTED) approach. Moreover, we incorporate concurrent learning (CL) to relax persistency of excitation condition and elastic weight consolidation (EWC) for lifelong learning on different tasks in the adaptive laws. Simulation results confirm theoretical conclusions.