Finite-Time Task-Space Coordinated Tracking of Networked Robotic Manipulators With Uncertain Dynamics and Disturbances

This article investigates task-space coordinated tracking in a finite time for networked robotic manipulators under uncertain dynamics and external disturbances. In contrast to the existing works by Liu and Chopra, Wang, Wang, Liang et al., and Zhang et al. that provide asymptotic convergence and require all the robots to have full access to the desired global task reference and communicate via either undirected or strongly connected graphs, the goal of this article is to propose an estimation and control framework so that task-space coordinated tracking can be achieved in a finite-time and robust manner over a directed graph with a spanning tree. Specifically, a supertwisting-based distributed estimator is developed first for each robot to estimate the desired global task trajectory in the finite time. By incorporating this estimated information, finite-time distributed adaptive control laws are designed to achieve task-space coordinated tracking irrespective of uncertain robot dynamics and disturbances. To demonstrate the effectiveness of the proposed approach, numerical simulations are presented.