RBFNN-Based Adaptive Sliding Mode Control Design for Delayed Nonlinear Multilateral Telerobotic System With Cooperative Manipulation

Abstract

Multilateral telerobotic system has potential applications in the industry environments with the advantages of cooperative manipulation for the remote and hazardous tasks, and its control design is quite challenging due to several coupling issues such as stability, position tracking, force feedback, and cooperative manipulation under time delays, various uncertainties, and external disturbance. In this paper, a novel radial basis function neural network (RBFNN) based adaptive sliding mode control design is proposed for nonlinear multilateral telerobotic system with n -master– n -slave manipulators. The environment force is modeled with a general form via the RBFNN-based environment parameters estimation in the slave side. The estimated environment parameters (nonpower signals) are transmitted to rebuild the environment dynamics in the master side and provide the good force feedback for the human operators. The RBFNN-based adaptive sliding mode controllers are designed separately for master and slave manipulators to achieve good position tracking under parameter variations and external disturbance. The coordinated force distribution algorithm is designed to achieve cooperative manipulation with the balance of force acting on the target object. The theoretical analysis is given and the comparative experiment for a nonlinear multilateral telerobotic system with 2-master–2-slave manipulators is implemented. The results show the good performance of our design.