Periodic event-triggered adaptive neural control of USVs under replay attacks

This work aims to address the control issue of unmanned surface vehicles (USVs) under replay attacks, where the influence of internal/external uncertainties and actuator’s physical constraints are taken into account. In the control design, a hyperbolic tangent function is used to replace the actuator saturation nonlinearity. To solve the design problem of mismatched unknown time-varying control gain caused by replay attacks, the single-parameter-learning technique is introduced, which avoids the operation of estimating for the unknown gain directly. To compensate for the effect of lumped uncertainties including attacks, unknown dynamics and external disturbances, the adaptive neural network with the finite covering principle is involved in the kinematic and dynamic channels, respectively. Furthermore, to reduce the update rate of actuator and alleviate the actuator wear, a periodic event-triggering mechanism (PETM) is established in the controller-actuator (C-A) channel. Finally, a periodic event-triggered (PET) adaptive neural tracking control solution for USVs under replay attacks is proposed, and it is verified that the control solution can force the trajectory of USVs to follow the reference trajectory even if there exists the effect of replay attacks. In addition, all signals in the closed-loop control system of USVs under replay attacks are bounded, and simulation and comparison results demonstrate the effectiveness of the control strategy.