Distributed Neural-Network-Based Cooperation Control for Teleoperation of Multiple Mobile Manipulators Under Round-Robin Protocol.

This article addresses the distributed cooperative control design for a class of sampled-data teleoperation systems with multiple slave mobile manipulators grasping an object in the presence of communication bandwidth limitation and time delays. Discrete-time information transmission with time-varying delays is assumed, and the Round-Robin (RR) scheduling protocol is used to regulate the data transmission from the multiple slaves to the master. The control task is to guarantee the task-space position synchronization between the master and the grasped object with the mobile bases in a fixed formation. A fully distributed control strategy including neural-network-based task-space synchronization controllers and neural-network-based null-space formation controllers is proposed, where the radial basis function (RBF) neural networks with adaptive estimation of approximation errors are used to compensate the dynamical uncertainties. The stability and the synchronization/formation features of the single-master-multiple-slaves (SMMS) teleoperation system are analyzed, and the relationship among the control parameters, the upper bound of the time delays, and the maximum allowable sampling interval is established. Experiments are implemented to validate the effectiveness of the proposed control algorithm.