Finite-time distributed formation control for multiple unmanned surface vehicles with input saturation

This paper investigates the finite-time distributed formation control for unmanned surface vessels (USVs) exposed to external disturbances, model uncertainties and input saturation constraints. By combing the sliding mode control method and adaptive algorithms, two control architectures are developed for USVs' formation control problem. Radial Basis Function Neural Networks (RBFNNs) is adopted for approximating the unavailable system dynamics, where the minimum learning parameter (MLP) algorithm is utilized to alleviate the excessive occupation of the computational resource. By feat of an auxiliary system, an adaptive mechanism is devised such that the input saturation problem could be figured out. It follows from the theoretical analysis that finite-time convergence is achievable under the proposed two controllers. Finally. numerical simulations are exhibited to illustrate the effectiveness of the proposed formation control schemes.

Automated summary

This paper investigates finite-time distributed formation control for unmanned surface vessels (USVs) facing external disturbances, model uncertainties, and input saturation constraints. By combining sliding mode control and adaptive algorithms, two control architectures are developed to address the formation control problem of USVs. Utilizing Radial Basis Function Neural Networks (RBFNNs) and an adaptive mechanism, the study demonstrates achievable finite-time convergence under the proposed controllers.