Distributed containment output-feedback control for a general class of stochastic nonlinear multi-agent systems

This study considers a distributed containment output-feedback control approach for a general class of stochastic uncertain nonlinear multi-agent systems. At first, local linear state observers are designed to deal with the unmeasured states. Then, radial basis function (RBF) neural networks (NNs) and minimal learning parameter approach are employed to approximate unknown nonlinearities. On the basis of dynamic surface control (DSC), command filter technique, adaptive neural approximator and linear observers, a simplified systematic approach to design of the coordinated containment output-feedback controller for stochastic uncertain nonlinear multi-agent systems is offered. In the proposed distributed controller the problems of explosion of complexity and effect of DSC filter errors are eliminated, simultaneously. Via Lyapunov theory, it is shown that the proposed controller can guarantee that all the signals in the closed-loop network system are cooperatively semi-globally uniformly ultimately bounded (CSGUUB) in the sense of mean square; meanwhile all followers׳ outputs converge to the dynamic convex envelope spanned by the dynamic leaders. Finally, simulation results are shown to confirm efficiency of the proposed method.