RBFNN-Based Adaptive Sliding Mode Control Design for Nonlinear Bilateral Teleoperation System Under Time-Varying Delays

Abstract

The bilateral teleoperation technique has drawn much attention with its attractive superiority to implement the tasks in hazardous environments. Transmission delays and uncertainties are the two main challenges in the nonlinear bilateral teleoperation system to guarantee stability and achieve good transparency performance (including position tracking and force feedback) simultaneously. In this paper, a radial basis function neural network (RBFNN)-based adaptive sliding mode control design is developed for the nonlinear bilateral teleoperation system with transmission delays and uncertainties. For details, the reference trajectory producer is designed in both the master and slave sides to produce the passive reference trajectories for the tracking of master/slave manipulators. The RBFNN-based adaptive sliding mode controller is designed separately for the master and slave to achieve the good tracking performance under system uncertainties. To mitigate the negative effect of transmission delays on the system's stability, a projection mapping by saturation function is applied in the master side to guarantee the boundedness of the delayed environmental torque. Thus, the global stability and the good transparency performance with both position tracking and force feedback can be simultaneously achieved for our proposed method. The comparative experiment is carried out, and the results show the significant performance improvement with our proposed control design.