Consensus of sliding mode observers for interval type-2 fuzzy descriptor fractional order multi-agent systems based on neural networks

This paper investigates the consensus of interval type-2 (IT2) fuzzy descriptor fractional-order multi-agent systems (DFOMASs) with adaptive sliding mode control based on neural networks. Firstly, a sliding mode observer (SMO) is designed for IT2 fuzzy DFOMASs. Then, a fuzzy distributed integral-type sliding mode surface is constructed. Next, a sufficient condition for the sliding mode to achieve consensus are given using linear matrix inequalities (LMIs). Moreover, an adaptive law based radial basis function neural network (RBFNN) is established to guarantee the reachability of the IT2 fuzzy DFOMASs. Finally, the effectiveness of the proposed method is demonstrated through numerical examples and simulations.