Introduction

With the development of the activities in deep sea, the application of the autonomous underwater vehicle (AUV) is very widespread and there is a prominent prospect. The development of an AUV includes many areas, such as vehicle (carrier/platform) design, architecture, motion control, intelligent planning and decision making, etc (Blidberg 1991; Xu et al., 2006). The researchers dedicate themselves to improving the performance of modular, low-cost AUVs in such applications as long-range oceanographic survey, autonomous docking, and shallow-water mine countermeasures. These goals can be achieved through the improvement of maneuvering precision and motion control capability with energy constraints. For low energy consumption, low resistance, and excellent maneuverability, fins are usually utilized to modify the AUV hydrodynamic force. An AUV with fins can do gyratory motion by vertical fins and do diving and rising motion by horizontal fins. Therefore, the control system of the propeller-fin-drived AUV is very different to the conventional only-propeller-drived AUV.

A dynamic mathematic model for the AUV with fins based on a combination of theory and empirical data would provide an efficient platform for control system development, and an alternative to the typical trial-and-error method of control system tuning. Although some modeling and simulation methods have been proposed and applied (Conte et al., 1996; Timothy, 2001; Chang et al., 2002; Ridley, 2003; Li et al., 2005; Nahon, 2006; Silva et al., 2007), there is no standard procedure for modeling AUVs with fins in industry. Therefore, the simulation of the AUVs with fins is a challenge.

This chapter describes the development and verification of a six Degree of Freedom (DOF), non-linear model for an AUV with fins. In the model, the external force and moment resulting from hydrostatics, hydrodynamic lift and drag, added mass, and the thrusters and fins are all analyzed and expressed in matrix form. The equations describing the rigid-body dynamics are left in non-linear form to better simulate the AUV inherently non-linear behavior. Motion simulation is achieved through numeric integration of the motion equations. The simulation output is then checked with the AUV dynamics data collected in experiments at sea. The comparison results show that the non-linear model gives an accurate estimation of the AUV's acutal motion. The research objective of this project is the development of WEILONG mini-AUV, which is a small, low-cost platform serving in a range of oceanographic applications (Su et al., 2007).

Due to the effect of fins, conventional control methods can not meet the requirement for motion control (Giusepp, 1999). It requires high response speed and robustness to improve the maneuverability, at the same time the controller's compute process should be simple enough. This chapter proposes a new control method which is adaptive to the AUV with fins —S surface control (Liu et al., 2001). S surface controller is developed from sigmoid function and the idea of fuzzy control which has been proved efficient in ocean experiments. It has a simple structure requiring only two inputs, but it is applicable to nonlinear system. Moreover, we will deduce self-learning algorithm using BP algorithm of neural networks for reference (Liu et al., 2002). Finally, experiments are conducted on WEILONG AUV to verify the feasibility and superiority.

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