Observer-based adaptive neural network dynamic surface bipartite containment control for switched fractional order multi-agent systems

This article studies the bipartite containment control problem for a class of fractional order nonlinear multi-agent systems in the presence of arbitrary switchings and unmeasured states. Under the framework of Lyapunov function theory, this article proposes an adaptive neural network dynamic surface controller, in which dynamic surface control technology can avoid "explosion of complexity" and obtain fractional derivatives for virtual control functions continuously. Radial basis function neural networks are used to approximate the unknown nonlinear functions and an observer is designed to obtain the unmeasured states. The proposed distributed protocol can ensure all the signals remain semi-global uniformly ultimately bounded in the closed-loop system and all followers can converge to the convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Example and simulation results confirm the feasibility of the proposed control method.