Quantum mechanics

Adaptive control

Control (management)

Engineering

Cartography

Convergence (economics)

Economics

Computer science

Saturation (graph theory)

Physics

Nonlinear system

Sliding mode control

Mathematics

Geography

Artificial intelligence

Combinatorics

Economic growth

Control theory (sociology)

Artificial neural network

Combing

Ocean Engineering

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.

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