Dataset of Finite-Time Output-Feedback Formation Control for High-Order Nonlinear Multiagent Systems With Obstacle Avoidance

This paper investigates the finite-time formation control problem for high-order nonlinear multiagent systems (MASs) with consideration of obstacle avoidance, unmeasurable states and dead-zone input. A neural networks  k-filter observer is designed to estimate the unmeasurable states and cope with the problem of dead-zone input. Also, by using a tangent type Lyapunov barrier function (LBF), the obstacle avoidance mission can be completed for MASs without dynamic mismatching. Then, combining finite-time method and the backstepping theory, an output-feedback finite-time controller is designed for high-order nonlinear MASs. Theoretical analysis indicates that the control algorithm can guarantee the boundedness of signals in system. The formation control and obstacle avoidance can also be accomplished in finite time by the presented control protocol. A simulation demonstrates the effectiveness of the proposed control algorithm. This dataset is the  is the simulation dataset