Path following of marine surface vehicles with dynamical uncertainty and time-varying ocean disturbances

et al. 2018

Applied Sciences

This paper proposes an adaptive trajectory tracking control strategy for underactuated unmanned surface vehicles subject to unknown dynamics and time-varing external disturbances. In short, the goal of this paper is to provide a control strategy that allows an underactuated unmanned surface vehicle to track a time dependent trajectory. First, a first-order sliding surface is introduced into the design of surge control law to converge to surge tracking error, and then a second-order sliding surface is hired to design yaw control law to deal with sway motion tracking error. Meanwhile, neural network minimum learning parameter method, which has a smaller amount of computation than a multilayer neural network, is employed to preserve the control law robustness against unknown dynamics and time-varing disturbances induced by wind, waves and ocean currents. Furthermore, much effort is made to obtain uniform ultimate bounded stability for the closed-loop control system. Finally, the numerical simulation experiments of straight line and circle trajectory tracking have been given to prove the correctness and feasibility of the proposed control strategy.

Section: Remarkmentioning

confidence: 99%

Adaptive Trajectory Tracking Control for Underactuated Unmanned Surface Vehicle Subject to Unknown Dynamics and Time-Varing Disturbances

et al. 2018

Applied Sciences

“…Consider the closed-loop system consisting of the USV dynamics (3), (9), the guidance laws (17), (18), the control laws (43), (49), the adaptive update laws (44), (50) and the neural shunting model (32). There exist appropriate designs κ, γ, k 1 , k ue , k re , γ u , κ u , ξ u , γ r , κ r , K eu , K er , ξ r , A, B, D, such that all error signals in the system are UUB.…”

Section: Stability Of the Closed-loop Systemmentioning

confidence: 99%

“…There is very much literature investigating the design of the path following controller, and different algorithms are introduced into it, such as classic and variant PID control [13,14], fuzzy control [15], sliding mode control [16], backstepping control [17], and so on. According to engineering practice, the ship model has the characteristics of nonlinearity and uncertainty due to the continuous change of operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation

et al. 2017

Applied Sciences

This paper addresses three related issues concerning the path following control of a podded propulsion unmanned surface vehicle (USV), namely modeling, guidance and control. The pod is different from the general propeller-rudder propulsion device, and its essence is a vector thruster. Therefore, first, through various assumptions and simplification, the three-degree of freedom (DOFs) planar motion model of the podded propulsion USV is established. Then, the classical line-of-sight (LOS) guidance strategy is improved by adaptive sideslip angle and a time-varying lookahead distance. Based on the guidance system, the corresponding controllers for yaw rate and surge speed are presented, which are combined by backstepping technology, the neural network minimum parameter learning method and the neural shunting model. Specifically, the neural network minimum parameter learning method is proposed to compensate the uncertainty of the model and the immeasurability of external disturbances, and the neural shunting model is employed to cope with the "explosion of complexity" problem of backstepping. Meanwhile, an auxiliary dynamic system is introduced to prevent actuator saturation (input saturation). All error signals of the system are proven to be uniformly ultimately bounded (UUB) by employing Lyapunov stability theory. Finally, two numerical simulations are given to prove the correctness of the proposed scheme.

“…Trajectory tracking aims at controlling USV to track a given time varying trajectory [5]. In the pathfollowing control scenario [6], USV is required to follow a path at a certain speed that is specified without temporal constraint. The contrast between trajectory tracking and path following is that the tracking error established by the latter is a function-mapping relationship between the system state and the reference path, which is more robust to the system.…”

Section: Introductionmentioning

confidence: 99%

Robust path‐following control based on trajectory linearization control for unmanned surface vehicle with uncertainty of model and actuator saturation

Qiu

IEEJ Transactions Elec Engng

et al. 2019

This article develops a novel path-following control strategy for underactuated unmanned surface vehicle (USV) subject to unmodeled dynamics and unknown multiple disturbance. A practical robust path-following controller is proposed using trajectory linearization control (TLC) technology, neural network, and auxiliary design system. First, the greatest advantage of this article is that the TLC technology is first introduced into the field of USV motion control, which provides a new direction for TLC technology research. Second, the underactuated model based on a transformation of the USV kinematics to Serret-Frenet frame is simplified by introducing a nonlinear coordinate transformation. Meanwhile, to improve the robustness and reduce the computational complexity, radial basis function neural network is replaced by neural network with minimum learning parameter method to compensate for unmodeled dynamics and unknown multiple disturbance. In addition, an auxiliary dynamic system is used to reduce the risk of actuator saturation. The stability of the whole system was proved based on the Lyapunov criteria. Finally, the comparison results demonstrate the superior performance of the proposed approach.