Control of Unmanned Surface Vehicle Along the Desired Trajectory Using Improved Line of Sight and Estimated Sideslip Angle

Abstract

In order to improve the accuracy and robustness of path following control for an Unmanned Surface Vehicle (USV) suffering from unknown and complex disturbances, a variable speed curve path following a control method based on an extended state observer was proposed. Firstly, the effect of the environmental disturbances on the USV is equivalent to an unknown and time-varying sideslip angle, and the sideslip angle is estimated by using the extended state observer (ESO) and compensated in the Line of Sight (LOS) guidance law. Secondly, based on the traditional LOS guidance law, the design of the surge velocity guidance law is added to enable the USV to self-adjust the surge velocity according to the curvature of the curve path, thus further improving the tracking accuracy. Finally, the heading and speed controller of the USV is designed by using a sliding mode control to track the desired heading and speed accurately, and then the path following control of the USV’s curve path is realised. Simulation results verify the effectiveness of the proposed method.

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

“…Compared with the literature [20,23], the rapidity of control has improved by more than 20%, and the accuracy of control has improved by more than 50%. The strong antiinterference has been proved under multi-interference, and the algorithm realized maintained the deviation at under 2% without a disturbance observer.…”

“…Thanks to its strongly autonomous navigation capability, environmental adaptability and modular design advantage, it has been widely used in hydrologic reconnaissance, maritime search and rescue, and navigation of formation and other fields [1][2][3][4][5][6]. To solve the nonlinear interference problem caused by the overlay and the interaction of multi interference, and the poor robustness of control methods due to the uncertainty of the model parameters [7][8], active disturbance rejection control [9][10] (ADRC), fuzzy control [11][12], backstepping control [13][14][15], sliding mode control [16][17][18][19][20][21] (SMC), and proportion integration differentiation (PID) control [22][23][24], combined with intelligent algorithms and artificial neural networks [25][26][27] (ANN), have been introduced by scholars.…”

Consistent Design of PID Controllers for an Autopilot

“…After a number of such corrections, the ship finally comes to the target, although along a trajectory looking like an arc hanging from the path. The objective of the advanced path-tracking algorithms [14][15][16][17][18]25] is to remove the arc completely or reduce it by including integration into the corrections.…”

An Improved Dynamic Surface Sliding Mode Method for Autonomous Cooperative Formation Control of Underactuated USVS with Complex Marine Environment Disturbances

“…In recent years, marine science and technology has been developed, in order to explore the ocean [1,2]. Meanwhile, because of the development and advancement of technologies in robotics and autonomous systems (RAS) [3], research on unmanned surface vessels (USVs) motion control has gradually become a hot topic [4][5][6][7], with USVs also playing an increasingly essential role in various fields, e.g. military defence, scientific research, environmental monitoring and so on [8][9][10].…”