Adaptive sliding mode consensus control based on neural network for singular fractional order multi-agent systems

Abstract

In this paper, a suitable state feedback sliding mode controller is designed for the singular fractional order multi-agent systems (SFOMASs) with uncertainty, in order to realize the consensus problem of multi-agent. First, the sliding mode of the designed SFOMAS is in the form of singular systems. The criterion for the admissible consensus of sliding mode is given by using linear matrix inequality (LMI), and an adaptive law based on radial basis function neural network (RBFNN) is established to ensure the accessibility of SFOMASs. Then, a special method is studied to make the sliding mode of the designed SFOMAS normalization. A sufficient condition for the stability and consensus of sliding mode is given by using LMI, and an adaptive law based on RBFNN is established to ensure the accessibility of SFOMAS. Finally, two numerical examples show the applicability of the proposed method.