Mutual Synchronization of Multiple Robot Manipulators with Unknown Dynamics

In this paper, we investigate the mutual synchronization control problem of multiple robot manipulators in the case that the desired trajectory is only available to a portion of the team members, and the dynamics and the external disturbances of the manipulators are unknown. Treating the weighted average of the outputs of the neighbors as the reference trajectory, an adaptive neural network (NN) tracking control is designed for each manipulator. Based on the Lyapunov analysis, rigid mathematical proof is provided for the proposed algorithm for both state feedback and output feedback cases. It is shown that, under the proposed adaptive NN control, the tracking error of each manipulator converges to an adjustable neighborhood of the origin. Simulations are provided to demonstrate the effectiveness of the proposed approach.