A Mode-Switched Control Architecture for Human-in-the-Loop Teleoperation of Multislave Robots via Data-Training-Based Observer

In this article, considering three different working modes (including the human-supervised, human-aided, and human-manned modes), we present a novel mode-switched control architecture for solving the human-in-the-loop (HIL) teleoperation problem of multislave robots with local slave-to-slave (S2S) communication, long-distance master-to-slave (M2S) communication and transmission delays. Throughout the control process, the S2S communication is updated following the event-triggered mechanism; meanwhile, the data transmission is fully distributed, namely, no global information can be transmitted. Besides, we also deal with the concerns of enhancing “telepresence,” namely, reconstructing the interaction force between a user-determined slave robot and its task environment at the human side, and then allow the human operator to control the multislave robots in a “virtual reality” way. To this end, by making full use of the historical and current data, the data-training-based (DTB) observer is designed to obtain the interaction force at the side of the slave robot and then assist the human operator to choose a proper control mode. The presented architecture is hierarchically designed for data collection, data processing and physical regulation, involving the DTB observer and the fully distributed event-based (FDEB) estimator in a unified framework. Finally, numerical examples are conducted to demonstrate the effectiveness of the architecture.