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While sailing near the sea surface, submarines will often undergo rolling motion caused by wave disturbance. Fierce rolling motion seriously affects their normal operation and even threatens their security. We propose a new control method for roll stabilization. This paper studies hybrid intelligent control combining a fuzzy control, a neural netwo...

## Contexts in source publication

**Context 1**

... a submarine is achieved by adjusting the propeller, bowplanes, sternplanes and rudder. The six degree-of freedom (DOF) model is illustrated in Figure 1. The six DOF system possesses three translational motions and three rotational motions. ...

**Context 2**

... sea condition was simulated with the following settings: average wave period Tw=12s, significant wave length 1/3 h =8m, damping coefficient =0.5, submarine speed V=10m/s and encounter angle =60 o . The performance of the controller for rudder roll stabilization was then tested in the simulation system, as shown in Figure 10. When the system is under measurement mode M1, the rudder roll stabilization controller is adopted to send out roll stabilization orders to control the rolling. ...

**Context 3**

... the system is under measurement mode M1, the rudder roll stabilization controller is adopted to send out roll stabilization orders to control the rolling. The simulation results are given in Figure 10. The solid line is the result of roll damping and the dotted line is the result without roll damping. ...

**Context 4**

... solid line is the result of roll damping and the dotted line is the result without roll damping. From Figure 10(a) we see that the roll reduction is approximately 52% in the sixth-grade sea condition. Figure 10(b) shows that the roll reduction is approximately 48% in the eighth-grade sea condition. ...

**Context 5**

... Figure 10(a) we see that the roll reduction is approximately 52% in the sixth-grade sea condition. Figure 10(b) shows that the roll reduction is approximately 48% in the eighth-grade sea condition. The simulation results show that the stabilization effect of the controller designed is relatively satisfied. ...

**Context 6**

... order to examine the robustness of the designed rudder roll stabilization controller, great perturbations have then been added to the initial model. The simulation results in Figure 11 show that, when the submarine model parameter changes significantly, the anti-rolling rate of the rudder roll stabilization controller is 46%. The experiment indicates that the controller designed has strong adaptability and robustness. ...

**Context 7**

... a result, the course control is adopted to reduce rolling. The result of the simulation is shown in Figure 12, which show that when the submarine travels at a speed of U=20m/s, the efficiency of the rudder roll damping controller is obviously higher than that of when the submarine travels at a speed of U=10m/s. When travelling at a low speed, the efficiency of the controller is reduced. ...

**Context 8**

... the following, suppose the speed is U=20m/s and the sea condition is tenth-grade. A simulated comparison is made in the rudder roll damping control system, as shown in Figure 13. As shown in Figure 12, in following sea =0°, the submarine travels in heading waves and the rolling amplitude of the submarines is at its smallest. ...

**Context 9**

... simulated comparison is made in the rudder roll damping control system, as shown in Figure 13. As shown in Figure 12, in following sea =0°, the submarine travels in heading waves and the rolling amplitude of the submarines is at its smallest. In quartering sea =45°, the submarine travels in partial heading waves and the rolling amplitude is slightly bigger than that in heading waves. ...

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... 1) The origin of a submarine is considered at its LCG. Fig 3.2 depicts the coordinate system used for a submarine [6]. E. The Equations to be used: 1) A very similar approach is followed for computing the submarine trajectory as was carried out for the ship. ...

Predicting a vessel's maneuvering behavior during the early design stages is of utmost importance. Usually, model testing is undertaken to estimate the hydrodynamic derivatives. However, during the early design stages, model testing may not be feasible, and one needs to depend on analytical or semi-empirical methods to predict their values. This study aims to develop a code for simulating the trajectory of a ship/submarine. The hydrodynamic derivatives can either be directly inputted or, in the absence of the hydrodynamic derivatives, especially during initial phases of design, the same is computed using the empirical formulas available in the open domain. A comprehensive study has been undertaken on the empirical formulae available in the open domain for predicting the hydrodynamic coefficients of ships, especially the non-linear terms. For computing the submarine hydrodynamic derivatives, the same has been calculated, considering the submarine to be a purely axisymmetric body having an ellipsoidal shape of the same length and diameter as the submarine. Trajectory simulations of the vehicle motion are obtained by solving the equation of motions utilizing the robust coding environment of MATLAB. For integrating the differential equations, Euler's method is used. The accuracy of the program results varied from 0.1% to 3%. It is hoped that the study carried out would be beneficial to naval architects involved in ship/submarine design studies.

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Ship autopilots play an important role in insurance of safe navigation and efficient transportation as else. For their successful design and development, many control techniques were and are being developed. In this paper, the application of artificial neural network (ANN) is investigated to design an autopilot for a surface ship. Feed forward multilayered architecture of ANN is used for approximation of the inverse model of the ship. The designed autopilot acts like an optimal one because of using a cost function for generation of ANN training data. The performance of designed autopilot is evaluated in still water and different wave frequencies. The stability and robustness of the designed system is proved through simulation, carried out in Matlab. The results show that the proposed autopilot can efficiently be used to control the course of a ship in a range of parameter variation.
1. Introduction
The main duty of an autopilot is to keep the course of ship in a predefined direction using mostly the rudder. The safety and efficiency of transportation are directly dependent on the performance of ship autopilots. For this, a lot of techniques like proportional-integral-derivative controller, optimal control theory, adaptive control, and nonlinear control are used to improve the autopilot performance. The major feature of these approaches is that they require the exact knowledge of dynamics of controlled objects, which is difficult to obtain in practice due to complexity of ship hydrodynamics. For adaptive and nonlinear control there are some demerits like difficult design and stability analysis. The mentioned above disadvantages can be overcome by using artificial neural network (ANN) because of ANN approximation ability of arbitrary smooth function with required accuracy (Haykin 1999; Terekhov 2002). The first way of application of ANN to ship steering is to use a conventional controller for training the neural network (NN) controller (Endo et al. 1989; Burns & Richter 1996; Unar 1999; Khizer et al. 2013; Pathan et al. 2012a, 2012b; Velagic 2006; Zirilli et al. 2000; Alarcin 2007; Alarcin et al. 2010). Another way to develop NN controller is to integrate NN with other control techniques in sense of adaptive control (Cao et al. 2000; Tzung-hang et al. 2001; Ming-chung & Zi-yi 2013; Li et al. 2004; Jun & Liu 2013; Xu et al. 2014; Xiao et al. 2011; Wang et al. 2013; Yang & Zhao 2006; Leonessa & VanZwieten 2004; Hu & Pan 2012).

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