Abstract
This paper investigates the path following control problem for an underactuated unmanned surface vehicle (USV) in the presence of dynamical uncertainties and time-varying external disturbances. Based on fuzzy optimization algorithm, an improved adaptive line-of-sight (ALOS) guidance law is proposed, which is suitable for straight-line and curve paths. On the basis of guidance information provided by LOS, a three-degree-of-freedom (DOF) dynamic model of an underactuated USV has been used to design a practical path following controller. The controller is designed by combining backstepping method, neural shunting model, neural network minimum parameter learning method, and Nussbaum function. Neural shunting model is used to solve the problem of “explosion of complexity,” which is an inherent illness of backstepping algorithm. Meanwhile, a simpler neural network minimum parameter learning method than multilayer neural network is employed to identify the uncertainties and time-varying external disturbances. In particular, Nussbaum function is introduced into the controller design to solve the problem of unknown control gain coefficient. And much effort is made to obtain the stability for the closed-loop control system, using the Lyapunov stability theory. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller.
Highlights
unmanned surface vehicle (USV) is attracting more and more attention of researchers from all over the world because of its extensive applications in the military and civilian areas [1]
This paper investigates the path following control problem for an underactuated unmanned surface vehicle (USV) in the presence of dynamical uncertainties and time-varying external disturbances
There are several ways to deal with control gain: (1) the parameter adaptive method is used to regulate control gain; (2) the neural network or fuzzy adaptive method is used to estimate control gain, but the problem is that these two methods difficultly avoid the singular problem; (3) no operation is done for the control gain
Summary
USV is attracting more and more attention of researchers from all over the world because of its extensive applications in the military and civilian areas [1]. In [9], a fully actuated vessel was exposed to a constant environmental force, and a sideslip angle compensation was introduced to design path following controller. The guidance strategy and path following controller can be extended to underactuated vessels, but the paper did not mention how to obtain sideslip angle. The work of [12] presented an extensive analysis of the ILOS guidance method for path following task of underactuated ships and showed that due to the embedded integral action the guidance law made the ships follow straight-lines by compensating for the sideslip angle effect of environmental disturbances. Motivated by the above-mentioned observations, the goal of this article is that, based on ALOS algorithm with a time-varying Δ, in the presence of external perturbations, model uncertainties, and unknown input gain, backstepping method, neural network minimum parameter learning method, neural shunting model, and Nussbaum function are used to design controller.
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