Human–Robot Coordination Control for Heterogeneous Euler–Lagrange Systems Under Communication Delays and Relative Position

Abstract

Human intelligence, cognition, and skills can be applied to robots to accomplish challenging tasks through cooperative control between humans and robots. In this article, we propose two control frameworks for the human teleoperation of multiple robots under undirected and directed graphs. The two control laws are considered for two cases: 1) when both the relative positions and velocities of the robots are available, and 2) when only the relative positions are available. In this article, we first propose a controller that integrates adaptive neural networks, task-space synchronization, and robust control to address several practical issues related to uncertainty in robotic systems, (e.g., model inaccuracy, time delay, and disturbance). Second, another control algorithm using only the relative position information is addressed by using distributed observers on the remote side. As a result, the system is proven to be stable, and the tracking errors of position and velocity are uniformly and ultimately bounded. Finally, the effectiveness of the proposed control algorithms is verified by experimental demonstration.