Fixed-time adaptive neural network synchronization control for teleoperation system with position error constraints and time-varying delay

Abstract This paper investigates the fixed-time master-slave synchronization control of teleoperation system with asymmetric position errors constraints, dynamics uncertainties and time-varying delay. First, we propose an adaptive fixed-time combined with Barrier Lyapunov Functions (BLF) controller to figure out asymmetric constraints issues, and it also applies to the case of teleoperation system with no constraint or symmetric state constraint requirements. Second, the adaptive radial basis function neural networks (RBFNNs) and linearly parameterizable control methods are used for dealing with the uncertainties and time-varying delay problems of system. Next, it is demonstrated that the globally fixed-time stability performance of teleoperation can be achieved through the proposed control strategy and the asymmetric constraint requirements of the position synchronization errors are met all the time. Finally, simulation validates the feasibility of the control method.