Periodic event-triggered adaptive neural output feedback tracking control of unmanned surface vehicles under replay attacks

Abstract

This paper proposes a periodic event-triggered adaptive neural output feedback tracking control scheme for unmanned surface vehicles under replay attacks, where actuator saturation constraint and internal/external uncertainties are involved. To reduce attack signals entering the control system, an independent adaptive neural state observer is developed to recover the unavailable real velocities and mismatched compound uncertainties. Under the backstepping design framework, the adaptive neural-based single-parameter-learning method is involved to reconstruct the internal/external uncertainties, and an anti-replay-attacks output feedback tracking control law is devised. Furthermore, in the controller-actuator channel, a smooth saturation model is introduced and a periodic event-triggering mechanism is established to relieve the physical constraint of actuators. Theoretical analysis and simulation results verify the effectiveness of the developed scheme.