Abstract: This paper describes a sliding mode controller for
autonomous underwater vehicles (AUVs). The dynamic of AUV
model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
To address these difficulties, a nonlinear sliding mode controller is
designed to approximate the nonlinear dynamics of AUV and
improve trajectory tracking. Moreover, the proposed controller can
profoundly attenuate the effects of uncertainties and external
disturbances in the closed-loop system. Using the Lyapunov theory
the boundedness of AUV tracking errors and the stability of the
proposed control system are also guaranteed. Numerical simulation
studies of an AUV are included to illustrate the effectiveness of the
presented approach.
Abstract: This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory using neural network model reference controller for a nontrivial mid-small size AUV "r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of high noise, and also can be concluded that the fast SA of similar AUV systems with economy in energy of batteries can be asserted during the underwater missions in search-and-rescue operations.
Abstract: This paper addresses the problem of trajectory
tracking control of an underactuated autonomous underwater vehicle
(AUV) in the horizontal plane. The underwater vehicle under
consideration is not actuated in the sway direction, and the system
matrices are not assumed to be diagonal and linear, as often found in
the literature. In addition, the effect of constant bias of environmental
disturbances is considered. Using backstepping techniques and the
tracking error dynamics, the system states are stabilized by forcing
the tracking errors to an arbitrarily small neighborhood of zero. The
effectiveness of the proposed control method is demonstrated through
numerical simulations. Simulations are carried out for an
experimental vehicle for smooth, inertial, two dimensional (2D)
reference trajectories such as constant velocity trajectory (a circle
maneuver – constant yaw rate), and time varying velocity trajectory
(a sinusoidal path – sinusoidal yaw rate).
Abstract: This paper focuses on a critical component of the situational awareness (SA), the neural control of autonomous constant depth flight of an autonomous underwater vehicle (AUV). Autonomous constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. The fundamental requirement for constant depth flight is the knowledge of the depth, and a properly designed controller to govern the process. The AUV, named VORAM, is used as a model for the verification of the proposed hybrid control algorithm. Three neural network controllers, named NARMA-L2 controllers, are designed for fast and stable diving maneuvers of chosen AUV model. This hybrid control strategy for chosen AUV model has been verified by simulation of diving maneuvers using software package Simulink and demonstrated good performance for fast SA in real-time searchand- rescue operations.
Abstract: A sophisticated simulator provides a cost-effective measure to carry out preliminary mission testing and diagnostic while reducing potential failures for real life at sea trials. The presented simulation framework covers three key areas: AUV modeling, sensor modeling, and environment modeling. AUV modeling mainly covers the area of AUV dynamics. Sensor modeling deals with physics and mathematical models that govern each sensor installed onto the AUV. Environment model incorporates the hydrostatic, hydrodynamics, and ocean currents that will affect the AUV in a real-time mission. Based on this designed simulation framework, custom scenarios provided by the user can be modeled and its corresponding behaviors can be observed. This paper focuses on the accuracy of the simulated data from AUV model and environmental model derived from a developed AUV test-bed which was jointly upgraded by DSTO and the University of Adelaide. The main contribution of this paper is to experimentally verify the accuracy of the proposed simulation framework.
Abstract: This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory for a nontrivial mid-small size AUV “r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of noises, and also can be concluded that the proposed research technique will be useful for fast SA of similar AUV systems in real-time search-and-rescue operations.
Abstract: This paper describes a feasibility study that is
included with the research, development and testing of a micro
communications sonobuoy deployable by Maritime Fixed wing
Unmanned Aerial Vehicles (M-UAV) and rotor wing Quad Copters
which are both currently being developed by the University of
Adelaide. The micro communications sonobuoy is developed to act
as a seamless communication relay between an Autonomous
Underwater Vehicle (AUV) and an above water human operator
some distance away. Development of such a device would eliminate
the requirement of physical communication tethers attached to
submersible vehicles for control and data retrieval.
Abstract: The control design for unmanned underwater vehicles (UUVs) is challenging due to the uncertainties in the complex dynamic modeling of the vehicle as well as its unstructured operational environment. To cope with these difficulties, a practical robust control is therefore desirable. The paper deals with the application of coefficient diagram method (CDM) for a robust control design of an autonomous underwater vehicle. The CDM is an algebraic approach in which the characteristic polynomial and the controller are synthesized simultaneously. Particularly, a coefficient diagram (comparable to Bode diagram) is used effectively to convey pertinent design information and as a measure of trade-off between stability, response speed and robustness. In the polynomial ring, Kharitonov polynomials are employed to analyze the robustness of the controller due to parametric uncertainties.
Abstract: An adaptive neural network controller for
autonomous underwater vehicles (AUVs) is presented in this paper.
The AUV model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
In this regards, a nonlinear neural network is used to approximate the
nonlinear uncertainties of AUV dynamics, thus overcoming some
limitations of conventional controllers and ensure good performance.
The uniform ultimate boundedness of AUV tracking errors and the
stability of the proposed control system are guaranteed based on
Lyapunov theory. Numerical simulation studies for motion control of
an AUV are performed to demonstrate the effectiveness of the
proposed controller.
Abstract: This paper presents recent work on the improvement
of the robotics vision based control strategy for underwater pipeline
tracking system. The study focuses on developing image processing
algorithms and a fuzzy inference system for the analysis of the
terrain. The main goal is to implement the supervisory fuzzy learning
control technique to reduce the errors on navigation decision due to
the pipeline occlusion problem. The system developed is capable of
interpreting underwater images containing occluded pipeline, seabed
and other unwanted noise. The algorithm proposed in previous work
does not explore the cooperation between fuzzy controllers,
knowledge and learnt data to improve the outputs for underwater
pipeline tracking. Computer simulations and prototype simulations
demonstrate the effectiveness of this approach. The system accuracy
level has also been discussed.