Consensus of Uncertain Second-Order Multi-Agent Systems Using Interval Type-2 Fuzzy System and Minimal Learning Parameter Algorithm

Abstract

In this paper, a new distributed adaptive consensus control scheme is proposed for uncertain second order nonlinear multi-agent systems (MASs) with unknown control gains. Interval type-2 fuzzy system (IT2FS) is invoked to approximate uncertain nonlinear dynamics of each agents and uncertain interaction between them. Also, minimal learning parameter (MLP) algorithm is applied to reduce the number of adaptive parameters and to avoid the “curse of dimensionality” problem. Then, the proposed distributed adaptive consensus control scheme is designed based on the dynamic surface control (DSC) approach. The proposed scheme avoids the “explosion of complexity” problem and it develops a control law without singularity concern. Stability analysis shows that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded. Simulation results illustrate the effectiveness of the proposed control approach.