Prescribed Performance Bipartite Consensus Control for MASs Under Data-Driven Strategy

Abstract

This paper investigates the bipartite consensus control problem for discrete time nonlinear multiagent systems (MASs) based on data-driven adaptive method. To begin with, a dynamic linearization strategy is utilized to establish the relationship between bipartite tracking error and control input for MASs. Secondly, the unknown parameter linearly associated with control input is acquired by the adaptive control approach, and a discrete time extended state observer is designed to estimate nonlinear uncertainties. Thirdly, in order to achieve the prescribed performance, the constrained bipartite consensus error is transformed through a strictly increasing function. Based on the converted equivalent unconstrained error function, a sliding mode controller using only the input and output data of the MASs is designed. Finally, the efficacy of the controller is confirmed by simulations.