Adaptive FNN Backstepping Control for Nonlinear Bilateral Teleoperation With Asymmetric Time Delays and Uncertainties

This paper proposes an adaptive fuzzy neural network backstepping control scheme for bilateral teleoperation systems with asymmetric time delays induced by the communication links and various uncertainties. Specifically, the control scheme adopts a kind of asymmetric structure to design respectively the fuzzy-neural-network adaptive controllers of the master and slave sides. For reducing the convergence time of trajectory error, instead of conventional tracking error signals, the velocity-based parameters is applied in the master side. And the backstepping controller in the slave side effectively avoids the acquisition of acceleration signal for engineering application. Meanwhile, the joint frictions of manipulators and the additive uncertainties of environmental parameters are substituted by the non-power approximate signals of the fuzzy logic algorithms, which copes with the passivity problem of the time-delayed channel. Under ignoring the upper bound information of the external disturbances, the asymptotically stability and trajectory tracking performance of the closed-loop system is analysed by the Lyapunov function. Finally, the experimental tests demonstrate that the closed-loop teleoperation system is stable under asymmetric time delays and possess a better joint position tracking performance than other neural network control schemes.