RBF-Neural-Network-Based Adaptive Robust Control for Nonlinear Bilateral Teleoperation Manipulators With Uncertainty and Time Delay

Abstract

The bilateral teleoperation system has raised expansive concern as its excellent behaviors in executing the tasks in the remote, unstructured, and dangerous areas via the cooperative operation systems. In this article, an radial basis function (RBF) neural network based adaptive robust control design is proposed for nonlinear bilateral teleoperation manipulators to cope with the main issues including the communication time delay, various nonlinearities, and uncertainties. Specifically, the slave environmental dynamics is modeled by a general RBF neural network, and its parameters are estimated and then transmitted for the environmental torque reconstruction in the master side. Since the parameters of the neural network (which are nonpower signals) are transmitted instead of the traditional environmental torque in the communication channel, the previous existing passivity problem under time delay is avoided. In both of master and slave sides, the trajectory creators are applied to generate the desired trajectories, and the RBF-neural-network-based adaptive robust controllers are designed subsequently to handle the nonlinearities and uncertainties. Theoretically, the proposed control algorithm can guarantee the global stability of bilateral teleoperation manipulators under time delay, and the good transparency performance with both position tracking and force feedback is also achieved simultaneously. The real platform comparative experiments are carried out, and the results show the good position tracking to execute precise operation and the good force feedback to detect the sudden disturbance in the environment dynamics.