Guidance and control methodologies for marine vehicles: A survey
Introduction
In recent years, a growing number of marine robotic vehicles including underactuated/overactuated marine vehicles, fully actuated unmanned surface/underwater vehicles, autonomous marine vehicles and unmanned wave gliders, have been developed for navigation, military, industry, and other practical applications (Ashrafiuon et al., 2010, Borup et al., 2020, Guzman et al., 2018, Karimi, 2018, Karimi, 2020, Vaddipalli et al., 2018, Vaerno et al., 2019, Wang, Li et al., 2019). The application-oriented guidance and control problem for marine robotic vehicles, as a fundamental issue, has long been a attractive focus of research drawing ongoing interest (Aguiary et al., 2009, Chin and Lum, 2011, Fossen et al., 2015, Moreno-Salinas et al., 2018, Panagou and Kyriakopoulos, 2013, Petrov et al., 2018, Yoshida et al., 1999). Over the past decades, with the rapid development of the science and technology, unceasing innovation of control theories and a wide range of practical applications, the research on guidance and control problems for marine vehicles has become an active research topic (Dong et al., 2016, Dong, Wang et al., 2019, Fu and Yu, 2018, Liang, Qu et al., 2020, Zhang, Sun et al., 2019). Owing to the special working environment, when the marine vehicles are moving, some undesired disturbances including unknown wind and waves, ocean currents, unknown internal dynamics as well as model uncertainties, may affect the dynamics of marine vehicles, which may deteriorate the performance of guidance and control for marine robotic vehicles (Ashrafiuon et al., 2010). In order to mitigate the impact of complex disturbances and system uncertainties, some robust methodologies coping with guidance and control problems for marine vehicles have been proposed, including fuzzy-based control design approach, neural network-based control design scheme, dynamic surface control strategy, feedback control technique, sliding model control method, backstepping control method, model predictive control technique, and other useful control approaches. In the past decades, some related research results have been surveyed in Abreu et al., 2016, Aguiar et al., 2009, Ashrafiuon et al., 2010, Ding et al., 2018, Johansen and Fossen, 2013, Qi et al., 2015 and Xiang, Yu, Lapierre, Zhang, and Zhang (2018).
The addressed guidance and control mainly embraces maneuvering, path following, trajectory tracking, formation control and consensus, which, in recent years, has attracted the ongoing research attention on the development of novel methodologies. In this survey paper, we mainly pay attention to guidance and control problems for marine robotic vehicles in the presence of complex external disturbances and model uncertainties and the objective is to give a systematic survey on some technological innovations for marine robotic vehicles. We specifically survey guidance and control issues under different control methodologies, such as, fuzzy-based control design approach, neural network-based control design scheme, dynamic surface control strategy, feedback control technique, sliding model control method, backstepping control method, model predictive control technique, and other control approaches. Finally, some conclusions are drawn and several possible related research directions are pointed out.
The remainder of this paper is organized as follows. In Section 2, some related definitions are stated. Guidance and control problems under different control strategies for marine vehicles is reviewed Section 3. Section 4 discusses some applications in practice. The conclusion and the possible work in the future are drawn in Section 5.
Section snippets
Some definitions
In this section, some definitions related with this review paper are given.
As shown in Fig. 1, a marine vehicle moves in 6 degree-of-freedoms (DOFs) along a seaway. , and denote the earth-fixed reference frame, , and denote the body-fixed reference frame. The explanations of notations shown in Fig. 1 is given in Table 1.
It is well known that, if the number of control input variables is less than the DOFs of the system, the system is treated as an underactuated one; if the
Guidance and control methodologies for marine robotic vehicles
How to safely and accurately control marine vehicles in the presence of complex external disturbances and system uncertainties has attracted much research attention due to its practical engineering significance and a number of methodologies, such as, fuzzy-based control design approach, neural network-based control design scheme, dynamic surface control strategy, feedback control technique, sliding model control method, backstepping control method, model predictive control technique, and so on,
Some practical applications
E-Navigation refers to, by electronic means, the integration of existing and new navigation equipments, to collect, synthesize, exchange, display and analyze maritime information on board and on shore, so as to enhance the navigation capacity of ships from berth to berth, enhance the corresponding maritime service, safety and security capabilities, as well as the ability of marine environmental protection, so as to better protect the marine environment and improve navigation safety and
Conclusion and future trends
An overview of guidance and control methodologies for marine vehicles has been provided in this survey paper. Although various control strategies have been well studied in the literature, however, with the rapid development of the computer science and network technology, some new techniques that can eliminate limitations from strict assumptions and special requirements have shown in the publications, which potentially bring some challenges for improvement over existing results and
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
This work was partially funded by the Italian Ministry of Education, University and Research through the Project “Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0”.
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