Resource-Constrained Adaptive Neural Output Feedback Security Control for Networked USVs Under Dual-Channel Malicious Attacks

This article focuses on the security control problem for the networked unmanned surface vehicles (USVs) under the network environment, where the malicious attacks (DoS attack and FDI attack) and external/internal uncertainties are taken into account. In the sensor-control channel, the event-sampled unit is involved to overcome the design problem of intermittent transmission signal caused by the DoS attack, and a novel event-sampled adaptive neural state observer (ESANSO) is developed to recover the velocity of USVs. In the control-actuator channel, a self-triggering control mechanism is proposed to save the communication resources. In the control design, with aid of the ESANSO and an adaptive neural approach utilizing a single-parameter leaning technique, a novel adaptive neural output feedback security control solution is developed. This approach effectively mitigates the impact of malicious attacks. The theoretical analysis shows that all signals are bounded, and the Zeno phenomenon can be avoided. Finally, the effectiveness of proposed control scheme is validated by simulation and experiments.