Euclidean distance damping–based adaptive sliding mode fault-tolerant event-triggered trajectory-tracking control

Abstract

This study investigates a novel event-triggered sliding mode fault-tolerant control system for underactuated remote-operated vehicles based on Euclidean distance to address the problems of sliding mode chattering, actuator faults, external uncertainty disturbance, and signal noise. In particular, the effects of high-frequency chattering and robust response to the control performance of the sliding mode are reduced by constructing a Euclidean error state mapping space and defining a novel Euclidean distance-based damped sliding mode reaching law. Subsequently, a Euclidean distance-based hyperbolic tangent fault-tolerant saturation parser is developed to resolve actuator faults and reduce saturation and signal noise. A neural network adaptive approximator is used in conjunction with finite-time techniques to estimate the upper bounds of external disturbances, system uncertainty, and Euclidean distance data of the saturation parser and improve the robustness of trajectory tracking. Moreover, finite time-bounded convergence of the signal of the closed-loop tracking system is ensured. Finally, an event-triggered mechanism that is not affected by the fault-tolerant mechanism is designed, and the communication resource occupation is optimized to improve the operational lifetime of the equipment and its transmission efficiency. The effectiveness of the proposed control system and its superiority over existing alternatives is demonstrated based on four sets of simulated comparisons.