Data-driven sliding mode tracking control with improved prescribed performance for a class of nonlinear discrete-time systems under DoS attacks and sensor faults

This article addresses the trajectory tracking control problem for single-input-single-output nonlinear discrete-time systems in the presence of sensor faults and denial of service (DoS) attacks. First, a DoS attack compensation mechanism is designed using a data-driven prediction method to mitigate the impact of DoS attacks on the system. Building on this, a fault detection mechanism is developed to detect sensor faults even when the system is under DoS attacks, with the fault information estimated using a radial basis function neural network for fault-tolerant control. Additionally, an improved prescribed performance function with adjustable parameters is incorporated to propose a novel data-driven sliding mode tracking control strategy. This strategy ensures that the system can achieve trajectory tracking and that tracking errors converge to a predefined asymmetric region, despite the presence of DoS attacks and sensor faults. Furthermore, the stability of the tracking error system is analyzed. Finally, the effectiveness of the proposed control strategy is validated through simulation results.