Fast finite time distributed adaptive formation control of underactuated ASVs

This paper investigates a distributed adaptive formation control for underactuated autonomous surface vessels(ASVs) based on the fast finite time theory. Besides, unknown environmental disturbances and model with partial nonlinear are considered in the process of controller design. By utilizing the experience of consensus control for multi-agent, the tracking errors and information transmission method among vessels are constructed through the algebra graph theory. In the control strategy, radial-basis-function neural networks(RBF NNs) and minimal learning parameter are used to account for the unknown nonlinear part of the system. Therefore, there are only two online parameters being tuned to tackle the uncertainties, which makes the control law more concise and reduces the calculation burden. One feature of the algorithm is that it adopts a first-order nonlinear filter instead of low-pass linear filter to design a novel dynamic surface control(DSC) scheme. It not only eliminates the explosion of complexity caused by repeated differentiations, but also satisfies the fast finite time stability. Based on the above methods, a distributed adaptive fast finite-time formation strategy is presented by combining directed topology and improved Lyapunov function, which effectively improve the convergence rate of the system, and all signals in the closed-loop system are semi-global uniformly ultimately bounded. Finally, the feasibility and effectiveness of the algorithm is verified by a simulation experiment.