Event-triggered-based adaptive asymptotic containment control using an improved DSC method for nonlinear MASs under unknown control directions

The article studies the adaptive event-triggered asymptotic containment control issue for a class of nonlinear nonstrict-feedback multi-agent systems (MASs) under unknown control directions. Firstly, the radial basis function neural networks (RBF NNs) are used to tackle the design challenges caused by the nonstrict-feedback structure and the completely unknown nonlinear functions. Then, this article has the two following merits: 1) the issue of “explosion of complexity” resulting from the continuous differentiation of virtual controllers is settled by proposing an improved dynamic surface control (DSC) method, and the influences of the boundary layers caused by the filters in the DSC procedure are eliminated skillfully through the compensation terms; 2) the event-triggered control (ETC) scheme is designed to decline the trigger frequency of the controllers, and Zeno behavior is triumphantly averted. The proposed controllers can assure that all the variables of closed-loop systems are uniformly ultimately bounded (UUB), and the containment errors eventually tend to zero. Finally, a simulation example demonstrates the feasibility of the presented scheme.