Dynamic event-triggered data-driven iterative learning bipartite tracking control for nonlinear MASs with prescribed performance

This article proposes a distributed dynamic event-triggered data-driven iterative learning control (DET-DDILC) scheme under a predefined performance to tackle the bipartite tracking control problem for multiagent systems (MASs). An improved dynamic linearization technique is utilized to convert the nonlinear MASs into an iterative linear data model. First, a peer-to-peer mapping function is introduced to map the constrained distributed system output homeomorphism to an unconstrained one. In addition, a DET mechanism based on a time-iteration-varying function is devised to conserve network communication resources. Based on the unconstrained transformation and the designed DET mechanism, the DET-DDILC algorithm is devised to ensure that the bipartite tracking performance of MASs can be within the preset range. Finally, the effectiveness and feasibility of the designed control scheme are demonstrated via a simulation case by a comparison.