Online Lifelong Dynamic Learning Control for Manipulators With Closed Architecture in Multi-Tasking Environments

This paper proposes an online lifelong dynamic learning-based outer-loop velocity compensation control scheme for$n$-degree-of-freedom robotic manipulators operating in continuous multi-task environments. A radial basis function neural network (RBF NN) incorporating a neuron dynamic-growing strategy is employed in the actual controller, enabling real-time adjustment of neuron compact sizes according to NN inputs and facilitating online identification of unknown system dynamics. Furthermore, an online weight feedback mechanism is integrated into the neural network learning law to preserve previously learned weight parameters during task execution. By introducing an S-shaped filtering function, significant synaptic weights are assigned higher feedback gains, whereas less important weights are gradually suppressed toward zero, effectively mitigating catastrophic forgetting. In contrast to conventional dynamic learning control approaches, the proposed scheme enables the retrieval of historical knowledge when online revisiting prior tasks, thereby ensuring sustained control accuracy over time. Rigorous theoretical analysis demonstrates that all closed-loop signals remain uniformly bounded, and both weight estimation errors and system identification errors converge exponentially to a small residual neighborhood around zero. Finally, experiments on a UR5 robotic manipulator validate the effectiveness of the proposed method.