Robust Time-Optimal Kinematic Control of Robotic Manipulators Based on Recurrent Neural Network Against Harmonic Noises

Industrial and service manipulators demand implementing time-optimal kinematic control to minimize task duration in a manner of maximizing end-effector velocity during path tracking. However, achieving this objective in the presence of harmonic noise while strictly enforcing joint motion constraints remains a significant challenge. This paper introduces a novel approach that leverages dynamic recurrent neural networks (RNNs) within a constrained optimization framework to deliver robust, time-optimal kinematic control even under harmonic disturbances. We provide a thorough theoretical analysis of the RNN-based control solver, establishing its convergence and optimality. Importantly, our method maximizes end-effector speed without violating any joint velocity limits, thereby enhancing the path-tracking speed compared to previous schemes. Simulation results and physical experiments further demonstrate the effectiveness and superiority of the proposed approach.