1950 Underwater Vehicle

The problem of power and communication limitation in underwater environment makes it more challenge to increase the degree of autonomy and intelligence for an autonomous underwater vehicle (AUV). An infrastructure of autonomous teleoperation platform for AUV is established and described, which allows control to be shared between the intelligent decision system in AUV system and operators throughout a mission.

In the (Paunicka J.L. et al., 2001, Wills L. et al., 2000, and Wills L. et al., 2003), the information-centric control and engineering have a remarkably successful history of enabling for designing, testing, and transitioning embedded software to unmanned air vehicle (UAV) platforms. A new software infrastructure called Open Control Platform (OCP) will accommodate in changing navigation information and control components, interoperate in heterogeneous environments, and maintain viability in unpredictable and changing environments. The OCP extends new advances in real-time middleware technology, which allows distributed hetero-geneous components to communicate asynchronously in realtime via CORBA middleware. It uses event-based distributed communication and it capable of transmitting events at different priorities. This enables highly decoupled interaction between the different components of the system, which tends to localize architectural or configuration changes that promising to be implemented quickly and high reliability in the real system. There are many examples of nice control algorithms for AUV which had done in several platforms (Valavanis K.P. et al., 1997), but in the implementation of those control systems in the sense of tightly coupling model in remote operation is widely open for sub-discipline of software engineering. We further investigate how the real-time control system performance could be reconfigured easily both in semi-automatically or manually interventions by remote station, and also develop a simulation platform to support a tuning mechanism of control parameters during runtime (i.e. feedback gains or trajectories) by using Matlab on separated machines connected via CORBA event-channel.

In this paper we organized as follows: Section 2 presents AUV dynamic model, physical values, and control algorithm. Section 3 gives the simulation systems design; include the hardware of simulation workstation, tools and interfaces, and middleware infrastructure. Section 4 presents results from the simulations together with the assumptions of problems solution. The last section covers conclusions.

2. Equations of motion 2.1 AUV dynamic model

The AUV Model for depth control is depicted in figure 1.

Fig. 1. AUV Model

The simple's form of equation of motion is obtained with body axes coincident with the principles axes of inertia, and the origin at the center of mass center of gravity (CG), for this case the equation in the dimensionless form as in (Sname 1950) are:

Fig. 1. AUV Model

The simple's form of equation of motion is obtained with body axes coincident with the principles axes of inertia, and the origin at the center of mass center of gravity (CG), for this case the equation in the dimensionless form as in (Sname 1950) are:

X = m[u + qw - ur] Y = m[u + ru - pw] Z = m[w + pv - qu]

The 6DOF components of the rigid body dynamic equations of motion of the submerged vehicle are:

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