Neural network adaptive control of teleoperation systems with uncertainties and time-varying delay

This paper studies the model uncertainties of Internet-based bilateral teleoperation systems under asymmetric time-varying delays. Generally, master and slave subsystems of a teleoperation process are robots with complex dynamics. This complexity makes the modelling process difficult, and usually with unavoidable uncertainties. Along with the latency through the communication network between the master and slave systems, the control problem becomes critical in terms of maintaining stability and performance of the system. Describing the modelling procedure and properties, this paper proposes an adaptive control scheme strengthened with a radial basis function (RBF) neural network (NN)-based algorithm to cope with the model uncertainties and also for stabilisation in the presence of time-varying delays. Using Lyapunov theorem, the stability analysis of the overall system under the proposed control method is investigated. Furthermore, simulation and experimental studies show the effectiveness and performance of this control for a teleoperation system.