Adaptive fixed-time backstepping control for three-dimensional trajectory tracking of underactuated autonomous underwater vehicles

Chen, Hongxuan; Tang, Guangze; Wang, Shufeng; Guo, Wei; Huang, Hui

doi:10.1016/j.oceaneng.2023.114109

Cited by 27 publications

(8 citation statements)

References 31 publications

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“…By integrating equation (26) with equations (24), ( 27) and (28), we arrive at the subsequent expression: Reformulating equation (25), the sliding surface ( ) s t can be presented as: Utilizing equation ( 26) and (29) we can represent the rudder input as:…”

Section: Line-of-sight Guidance and Controllers Of The Auvmentioning

confidence: 99%

“…By integrating equation (26) with equations (24), ( 27) and (28), we arrive at the subsequent expression: Utilizing equation ( 26) and (29) we can represent the rudder input as: When heading error,  y ( ) ( ) t CET i goes beyond 90°, AUV is likely to traverse the track in a direction contrary to what's intended. To avoid this scenario in real-world applications, a 40-degree threshold has been established, beyond which the system transitions to line-of-sight (LOS) control.…”

Section: T X X T 17 Wpt I Wpt Imentioning

confidence: 99%

“…The primary methodologies to address AUV trajectory tracking include sliding mode control [18][19][20], cascade system approach [21][22][23], back-stepping method [24][25][26], robust control [27], switching control [28], and Lyapunov's direct technique [29,30], among others. Yan et al [31] proposed a dynamic formation model for underwater environments, supplemented with algorithms specifically designed for such intricate conditions.…”

mentioning

confidence: 99%

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AUV way-point tracking at constant velocity: cross-track error (CTE) and line-of-sight (LOS) guidance

Ghosh,

Mandal

2024

Eng. Res. Express

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This paper addresses the integrated challenges of path-following and tracking control for an under-actuated Autonomous Underwater Vehicle (AUV) in the two-dimensional (2-D) plane. Four distinct 2-D trajectories: linear, linear with sharp turns, curved, and circular trajectories have been considered in this study. The proposed path-following control algorithm leverages AUV kinematic and dynamic models, incorporating the Cross-Track Error (CTE) method and Line-of-Sight (LOS) technique to determine the desired orientation. Stability analysis has been performed to evaluate the robustness of the controllers against sudden underwater disturbances. Additionally, perturbation has been introduced in simulations to mimic real-world conditions more accurately. The simulations confirm the controllers' proficiency in accurately tracking these various trajectories from a given starting point.

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Section: Line-of-sight Guidance and Controllers Of The Auvmentioning

confidence: 99%

Section: T X X T 17 Wpt I Wpt Imentioning

confidence: 99%

mentioning

confidence: 99%

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AUV way-point tracking at constant velocity: cross-track error (CTE) and line-of-sight (LOS) guidance

Ghosh,

Mandal

2024

Eng. Res. Express

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“…The robustness of the proposed strategy was verified with considerations of uncertain hydrodynamic parameters. Chen et al [26] studied an adaptive timing back-stepping method for threedimension trajectory tracking control of under-actuated AUVs under undetermined model and external interference. The back-stepping method was used to deduce the virtual velocity guidance law.…”

Section: Introductionmentioning

confidence: 99%

A LSSVR Interactive Network for AUV Motion Control

Jiang

Wan

Zhang

et al. 2023

JMSE

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In view of the requirements on control precision of autonomous underwater vehicles (AUVs) in different operations, the improvement of AUV motion control accuracy is the focus of this paper. In regard to the unsatisfying robustness of traditional control methods, an interactive network based on Least Square Support Vector Regression (LSSVR) is therefore put forward. The network completed the identification of the strong nonlinear AUV dynamic characteristics based on the LSSVR theory and by virtue of the interactions between the offline and online modules, it achieved offline design and online optimization of the AUV control law. In addition to contrastive numerical simulations and sea trials with the classic S-plane method in AUV velocity and heading control, the LSSVR network was also tested in path following and long-range cruise. The precision and robustness and of the proposed network were verified by the high-accuracy control results of the aforesaid simulations and trials. The network can be of practical use in AUV control especially under unfamiliar water conditions with access to a limited number of control samples or little information of the operation site.

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“…The strong robustness of adaptive control can effectively cope with the uncertain disturbance caused by AUV underwater movement. The model reference adaptive control (MRAC) [18] can identify unknown parameters in the system and disturbance online [19]. According to the identified parameters, the control law is adjusted to eliminate the disturbance of the external environment to the controlled object [20,21], so that the output of the closed-loop system of the controlled model can follow the output of the ideal reference model, and the anti-disturbance ability of the system is improved [22].…”

Section: Introductionmentioning

confidence: 99%

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

Yuan,

She,

Zhang

et al. 2023

JMSE

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This study focuses on addressing the the coupling problem of the vertical and horizontal plane of an autonomous underwater vehicle (AUV) with an X-rudder. To guarantee the steering performance of the AUV, a depth and course control algorithm based on an L1 adaptive control algorithm (L1AC) is proposed. Firstly, the linear quadratic regulator (LQR) is designed on the basis of an AUV vertical and horizontal linear model, and the system has optimal control quality for both of the nominal vertical and horizontal models. Secondly, the nonlinear state predictor, disturbance estimator, and adaptive controller are designed separately, which is used for the compensation of the uncertainty and disturbance of the AUV dynamic. Finally, the rudder angle allocation algorithm is designed to realize the rudder angle command of the cross rudder to the X-rudder. The simulation and experiment results show that the adaptive control algorithm proposed in this paper can effectively control AUV to complete high-performance course and depth control. Based on the experiments, the prospects and shortcomings of the L1AC, LQR and MRAC algorithm in AUV control are compared and analyzed.

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Adaptive fixed-time backstepping control for three-dimensional trajectory tracking of underactuated autonomous underwater vehicles

Cited by 27 publications

References 31 publications

AUV way-point tracking at constant velocity: cross-track error (CTE) and line-of-sight (LOS) guidance

AUV way-point tracking at constant velocity: cross-track error (CTE) and line-of-sight (LOS) guidance

A LSSVR Interactive Network for AUV Motion Control

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

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