Robust adaptive self-structuring neural networks tracking control of unmanned surface vessels with uncertainties and time-varying disturbances

The trajectory tracking control problem of unmanned surface vessels (USVs) with uncertainties and time-varying disturbances is investigated. A robust adaptive trajectory tracking control scheme is proposed based on finite-time H-infinity control and a self-structuring neural networks (SSNN) identifier, which can obtain satisfactory performance with an L-2 norm-bounded, expected attenuation level within a finite time. The SSNN is developed to approximate USVs system uncertainties and external disturbances by online learning. Most importantly, a balance is achieved between the optimal number of neurons and the expected performance, which saves significant network resources. The Lyapunov stability analysis shows that the scheme ensures convergence of the tracking error to a small neighborhood around zero in finite time, while all the other closed-loop signals remain bounded. Moreover, the application of a high-gain observer effectively reduces the cost of velocity sensors. The feasibility and effectiveness of this control scheme are verified by theorem analysis and numerical simulations.