Neural observer-based fixed-time formation control of multiagent systems

In this paper, the formation control problem for nonlinear multiagent systems (MASs) with immeasurable velocities in the directed communication topology is investigated under the constraints of external disturbances and dynamic uncertainties. To tackle these issues, a neural observer combining an extended state is constructed, utilizing fixed-time sliding mode and adaptive radial basis function (RBF) neural network to significantly enhance the estimation accuracy and convergence rate. Subsequently, a fixed-time formation control strategy is designed based on the feedback information from the neural observer. This strategy also incorporates a centralized event-triggered mechanism to prevent Zeno behavior and reduce the frequency of controllers' updates. The global fixed-time convergence of the closed-loop MASs is proven through Lyapunov theory. Finally, comparative simulations involving six omnidirectional robots substantiate the effectiveness of the proposed control algorithm.