Dynamic event-triggered output consensus of nonstrict-feedback MASs with predefined performance

This paper deals with the output consensus problem for uncertain nonstrict-feedback leader-follower multi-agent systems with predefined performance. A distributed event-triggered control strategy with dynamic threshold is proposed to update the actual control input and alleviate the computation burden of the communication procedure effectively. The unknown nonstrict-feedback structures are addressed by using the property of radial basis function neural networks. It is worth noting that in practical applications, the predefined performance often alternates between constrained and unconstrained cases in some extreme situations. To overcome this challenge, a novel coordinate transformation technique is incorporated to tackle both the two cases with and without performance constraint in a unified manner. As a result, the proposed event-triggered control approach ensures that the output consensus errors converge to zero asymptotically, and all the signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed protocol is demonstrated by the simulation results.