Self-triggered adaptive neural control for USVs with sensor measurement sensitivity under deception attacks

Abstract

This article investigates the control problem of unmanned surface vessels with sensor measurement sensitivity under deception attacks, and proposes a novel self-triggered adaptive neural control scheme under the backstepping design framework. To solve the control design problem of unknown time-varying gains caused by deception attacks and measurement sensitivity in kinematic and kinetic channels, the parameter adaptive and neural network technology are involved. In addition, to decrease actuator wear caused by the high-frequency wave and sensor measurement sensitivity and reduce the computational burden caused by continuous monitoring of the triggered condition, a self-triggered mechanism is constructed in the controller–actuator channel. Finally, a self-triggered adaptive neural control solution is proposed, which can guarantee that all signals in the whole closed-loop system are bounded by theoretical analysis. The effectiveness and superiority are verified by numerical simulations.