Neural-network-based event-triggered adaptive secure fault-tolerant containment control for nonlinear multi-agent...

Under the framework of backstepping theory, dealing with the non-differentiable problem of virtual control signals caused by sensor output triggering is difficult. Meanwhile, it is of great practical significance to consider problems of output triggering, multiple faults, and denial-of-service (DoS) attacks in nonlinear multi-agent systems (MASs). This paper studies a neural-network-based event-triggered adaptive secure fault-tolerant containment control problem for nonlinear MASs under multiple faults and DoS attacks. Under sensor output triggering, only intermittent output signals are used to construct a switched neural network estimator to guarantee that estimated states are first-order derivable. Meanwhile, virtual control laws are constructed using estimated states to ensure first-order differentiable, and dynamic filtering technology is adopted to avoid the repeated differentiation of virtual control laws. It is shown that the designed secure fault-tolerant containment controller can compensate for faults and DoS attacks, and each follower can converge to a dynamic convex hull spanned by multiple leaders. Practical simulation results are given to verify the effectiveness of the proposed control method.