Self-triggered fixed-time bipartite fault-tolerant consensus for nonlinear multiagent systems with function constraints on states

This article presents a self-triggered bipartite fault-tolerant consensus control scheme in a fixed-time stability sense for multiagent systems (MASs) with function constraints on states. Neural networks are introduced to identify unknown nonlinearities, thereby relaxing constraints on unknown functions. Then, based on time varying barrier Lyapunov functions including time and previous states, all states of the considered MASs are constrained to given ranges. In addition, to improve the utilization of system transmission resources, self-triggered control method in which the next triggering time can be computed according to the current information of the controller is proposed. By applying the Lyapunov theory, the designed self-triggered controller can ensure that all signals containing consensus tracking errors are fixed-time bounded under given communication topology, and the Zeno phenomenon is successfully avoided. Finally, a practical example is provided to testify the feasibility of the developed control scheme.