Practical Fixed-Time Adaptive Neural Synchronization Control for Bilateral Teleoperation Systems With Asymptotic Convergence

In this paper, a fixed-time adaptive neural synchronization tracking control for uncertain bilateral teleoperation systems is studied. First, a novel practical fixed-time stability criterion is presented, based on which the settling time does not depend on the initial conditions. Second, the uncertain functions in the bilateral teleoperation system are approximated by radial basis function neural networks. Then, a fixed-time adaptive control scheme is developed based on backstepping technique, which guarantees that all signals in the closed-loop system are bounded and the synchronization tracking errors can reach zero as time tends to infinity. Meanwhile, the singularity of the control signal is completely circumvented. Finally, the simulation results are given to validate the feasibility of the proposed scheme.