Consensus control protocol for stochastic multiagents with predictors

In this work, a consensus control strategy is designed for stochastic multiagents system with a leader under directed topological diagrams. In the process of designing the controller, a neural networks structure is approximately used instead of uncertain functions. A novel consensus scheme with predictors is established via dynamic surface programme. Furthermore, according to backstepping technique and Lyapunov stability theorem, it can be concluded that our scheme can obtain a rapid learning effect, while the expected tracking is achieved within a small error range.