Neuro-Observer Based Adaptive Fixed-Time Fault-Tolerant Control for Uncertain Teleoperation System With Input Saturation and Asymmetric Time-Varying Output Constraints

The issue of master-slave synchronization tracking control for uncertain teleoperation systems with input saturation and asymmetric time-varying output constraints under actuator failure is investigated. Firstly, a neural network-based nonlinear disturbance observer is designed to mitigate the negative impact of the compound uncertainty. Secondly, asymmetric barrier functions are introduced in the design of virtual control terms to implicitly handle the asymmetric time-varying output constraints issue. Following this, we leverage adaptive techniques to solve the problem of unknown control gains induced by actuator failure and develop an adaptive fixed-time fault-tolerant control approach. Using the Lyapunov stability theorem and fixed-time theory, it is rigorously proven that the closed-loop system is practically fixed-time stable and the system output is strictly confined within the asymmetric time-varying constraint boundaries. Finally, two typical actuator failure scenarios are considered during numerical simulations to validate the effectiveness and comprehensive performance of the developed approach.