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This paper investigates the finite-time formation control problem for high-order nonlinear multiagent systems (MASs) with the 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 an integral-multiplicative tangent Lyapunov-barrier function (LBF), the obstacle avoidance mission can be completed for MASs without dynamic mismatching. Then, combining the 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 not only make agents accomplish the formation obstacle avoidance task but also guarantee the boundedness of signals in the system. A simulation demonstrates the effectiveness of the proposed control algorithm. Note to Practitioners—This article studies the formation obstacle avoidance problem for MASs which plays an important role in transportation, exploration and rescue activities. Unmeasurable states and dead-zone input, which often occur in practice, are also considered and handled by a neural networks k-filter observer. An integral-multiplicative tangent LBF is utilized to complete the obstacle avoid task which is necessary for actual systems. Moreover, to be more feasible in industrial applications, the finite-time control strategy is used in the controller design.
Wang et al. (Wed,) studied this question.