Robust Neural Network Trajectory-Tracking Control of Underactuated Surface Vehicles Considering Uncertainties and Unmeasurable Velocities

Abstract

This article focuses on the trajectory-tracking of an underactuated surface vehicle (USV) considering model uncertainties and nonlinear environmental disturbances. For trajectory tracking in an actual USV sailing environment, both the inertia and damping matrixes are not diagonal, the velocities states are unmeasurable, and error constraints and input saturation are considered. A robust control strategy is proposed based on the backstepping method, state transformation, a super-twisting state observer, and neural networks. All the closed-loop signals are uniformly ultimately bounded, which is proved by the Lyapunov stability theory analysis. The advantages of the proposed method are as follows. (i) A super-twisting observer is constructed to solve the problem of the velocities being unmeasurable, and the error between the virtual and actual velocities converges to a small neighborhood around zero. (ii) Additional controllers are developed to address input saturation of the system control. (iii) A predefined function design is employed to guarantee the transient trajectory-tracking performance. Finally, simulation results verify the feasibility and effectiveness of the proposed USV trajectory-tracking control method.INDEX TERMS Underactuated surface vehicle, trajectory tracking, prescribed performance, neural network, output feedback control.

Fixed-Time Formation Tracking Control of Multiple Unmanned Surface Vessels Considering Lumped Disturbances and Input Saturation

“…Theorem 1. The proposed disturbance observer ASTLDO ( 16) can precisely observe and compensate the lumped disturbances for the system (12) with lumped disturbances by the designed adaptive laws ( 18) and ( 19) so that the observation error converges to zero in a finite time.…”

“…Due to the existence of approximate residuals in the neural network algorithm, it can only obtain globally uniformly bounded tracking performance rather than asymptotically stable performance. In [12], a trajectory tracking controller is proposed for underactuated surface vehicles, considering uncertainties and unmeasurable velocities. The system uncertainties are estimated by RBF neural network, and simulation results show the effectiveness of the proposed method.…”

The Non-Singular Terminal Sliding Mode Control of Underactuated Unmanned Surface Vessels Using Biologically Inspired Neural Network

“…Underactuated Unmanned Surface Vessels (USVs), characterized by their small size, light weight, and good stealth, have been widely used in many civil and military areas. These autonomous systems are primarily utilized for tasks that are dangerous or unsuitable for human operators [1][2][3], and many methods and technologies have been proposed and developed, such as trajectory tracking control [4], path tracking control [5], and power localization control [6] in hazardous waters.…”

Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode

“…In [14], An adaptive backstepping controller is mentioned to estimate the entire nonlinear damping and disturbances simultaneously without accurately knowing the parameter vector dimension of model uncertainty. Zou et al used RBF neural network to estimate the model parameter uncertainty, and adopted backstepping control and predefined performance functions to converge the tracking error [28]. Similarly, Qiu et al constructed a radial basis neural network using the minimum learning parameter(MLP) method to approach the uncertain system dynamics online [20].…”