Marine Control Systems: Guidance, Navigation And Control Of Ships, Rigs And Underwater Vehicles

Marine Control Systems: Guidance, Navigation And Control Of Ships, Rigs And Underwater Vehicles

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The Marine Systems Simulator (MSS) is a Matlab and Simulink library for marine systems. It includes hydrodynamic models for ships, underwater vehicles, and floating structures. The library also contains guidance, navigation, and control (GNC) blocks for real-time simulation. The algorithms and methods are described in:

As for autonomous surface vehicle systems, the modeling process is time-consuming and a large number of experiments is required for identifying model parameters. On the other hand, robustness against model uncertainty and ocean disturbances is critical for high-performance control of ASVs (Fossen, 2002; Skjetne et al., 2005; Tee and Ge, 2006; Li et al., 2008; Dai et al., 2012; Chen et al., 2013; How et al., 2013). To deal with this problem, adaptive backstepping and DSC techniques has been widely suggested; see the references (Fossen, 2002; Skjetne et al., 2005; Tee and Ge, 2006; Li et al., 2008; Dai et al., 2012; Chen et al., 2013; How et al., 2013). In Tee and Ge (2006), a stable tracking control method is proposed using backstepping and Lyapunov synthesis for multiple marine vehicles under the unmeasurable states. In Chen et al. (2013), a variable control structure based on backstepping and Lyapunov synthesis is designed for the positioning of marine vessels with the parametric uncertainties and ocean disturbances. In How et al. (2013), an adaptive approximation technique is designed using the backstepping to estimate the uncertainties. In Dai et al. (2012), an adaptive neural networks control method is designed based on the backstepping and Lyapunov synthesis with uncertain environment. In Skjetne et al. (2005), an adaptive recursive control method is designed using the backstepping and Lyapunov synthesis for marine vehicles with the unknown model parameters. Although the adaptive backstepping and DSC are recursive and systematic design methods, it does not offer the freedom to choose the parameter adaptive laws (Krstić et al., 1995). Besides, the identification process depends on the tracking error dynamics, and the transient performance cannot be guaranteed (Cao and Hovakimyan, 2007; Yucelen and Haddad, 2013).

Thor Inge Fossen (born January 3, 1963) is a Norwegian cyberneticist. Fossen received the MSc degree in Marine Technology (1987) and PhD in Engineering Cybernetics (1991) both from the Norwegian University of Science and Technology (NTNU). He is a Fulbright alumni and he pursued postgraduate studies in Aerodynamics and Aeronautics at the Department of Aeronautics and Astronautics of the University of Washington, Seattle (1989-1990). At age 28 he was appointed associated professor of guidance, navigation and control at NTNU and two years later he qualified as full professor. He has been elected member of the Norwegian Academy of Technological Sciences since 1998 and elevated to IEEE Fellow (2016) for his contributions to modeling and controlling of marine craft.[1] Fossen is one of the founders of the company Marine Cybernetics (2002), which was acquired by DNV GL in 2014. He is co-founder of the company SCOUT Drone Inspection AS (2017) and he is involved in several new high-tech companies in Trondheim. He is currently co-director of the NTNU Center for Autonomous Marine Operations and Systems. He has made contributions in the areas of marine craft motion control systems, hydrodynamics, control theory, guidance systems and navigation.

Fossen's field of research is control theory, computer science, navigation and marine hydrodynamics. He has published approximately 400 papers on guidance, navigation and control (GNC), vehicle dynamics, and control systems for ships, underwater vehicles and unmanned vehicles. He has authored three textbooks. The first textbook [2] has become the standard reference in marine control systems. This book was followed up by two textbooks [3] and [4] The mathematical models for marine craft GNC systems are based on a robot-inspired model representation first published in 1991.[5] Fossen's marine craft and ocean vehicle models have become a standard for marine craft motion control systems design. In addition to the three textbooks, Fossen has co-authored three editorials,[6][7] and[8]

This paper presents a modelling and adaptive controlling approach for the complex-shaped AUV. In Section 2, the standard notions for marine vehicles are introduced. Section 3 is focused to develop the model of the complex-shaped AUV to find the hydrodynamic parameters using the computational approach, ANSYS FLUENTTM. In Section 4, the vehicle-fixed frame adaptive controller is introduced to track the desired trajectories under the presence of hydrodynamic uncertainties, and uses the quaternion-based attitude error. Finally, the path planning method is developed based on the trajectory tracking controller, and the approach to find the minimum thrust allocation is proposed to increase the effectiveness of the propulsion system in Section 5. To the best of our knowledge, this is the first time that the representation of the quaternion-based attitude error is used for the trajectory tracking and path planning with a complete hydrodynamic analysis.

However, the above literature is mainly aimed at the depth control of the trawl net. To ensure the safety of the semi-pelagic trawl in a complex environment, trajectory tracking control in three-dimensional space is required. Moreover, the motion state parameters of two otter boards should be seriously considered because they provide the lateral spread of the trawl net. Few studies have investigated three-dimensional tracking of the semi-pelagic trawl. Nevertheless, many approaches have been proposed to control the trajectory of under-actuated unmanned underwater vehicles(UUV), which are quite similar to the research question in this work. The commonly used tracking control methods of UUVs are neural network adaptive control, sliding-mode control and the backstepping method. Yu et al. presented a direct adaptive control algorithm for UUVs based on a generalized dynamic fuzzy neural network [11J.C. Yu, Q. Li, and A.Q. Zhang, "Neural network adaptive control for underwater vehicles", Cont. Theory App., vol. 25, no. 1, pp. 9-13, 2008.]. Jia et al. approached a virtual guide method to establish the space motion error equation of UUVs, and a three-dimensional path controller was designed based on nonlinear iterative sliding mode [12H.M. Jia, L.J. Zhang, and X.Q. Cheng, "Three dimensional path following control for an underactuated UUV based on nonlinear iterative sliding mode", Acta Autom. Sin., vol. 38, no. 2, pp. 308-314, 2012.[ ] ]. Do et al. adopted the Lyapunov direct method and backstepping technique to design an adaptive path tracking controller of UUVs, which considered the parameter uncertainty of the system model [13K.D. Do, J. Pan, and Z. Jiang, "Robust and adaptive path following for underactuated autonomous underwater vehicles", Ocean Eng., vol. 31, no. 16, pp. 1967-1997, 2004.[ ] ]. Jon et al. combined the backstepping method and feedback control model to control slender-body under-actuated UUVs and carried out an experiment [14E.R. Jon, J.S. Asgeir, and Y.P. Kristin, "Model-based output feedback control of slender-body underactuated AUVs: theory and experiments", IEEE T. Cont. Syst. Tech., vol. 16, no. 5, pp. 930-946, 2008.[ ] ]. Zhu et al. proposed a backstepping tracking control algorithm for UUVs combined with a bio-inspired neurodynamics model, which reduced the speed jump in the conventional backstepping controller [15D.Q. Zhu, and R.R. Yang, "Backstepping tracking control of autonomous underwater vehicles with bio-inspired neur dynamics model", Cont. Syst. Tech., vol. 29, no. 10, pp. 1309-1316, 2012., 16B. Sun, D.Q. Zhu, and Z.G. Deng, "Bio-inspired discrete trajectory-tracking control for open-frame underwater vehicles", Cont. Sys. Tech., vol. 30, no. 4, pp. 454-462, 2013.]. Xu et al. applied a virtual velocity to replace attitude tracking in the backstepping design, and adaptive sliding model control was adopted to increase the adaptive ability of UUVs in dynamic uncertain environments [17J. Xu, M. Wang, and L. Qiao, "Backstepping-based controller for three-dimensional trajectory tracking of under-actuated unmanned underwater vehicles", Cont. Syst. Tech., vol. 31, no. 11, pp. 1589-1596, 2014., 18J. Xu, M. Wang, and L. Qiao, "Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles", Ocean Eng., vol. 105, pp. 54-63, 2015.[ ] ]. 2b1af7f3a8