This paper presents a six-degrees-of-freedom controller for autonomous underwater vehicles. The control algorithm is adaptive in the dynamic parameters that are poorly known and time-varying in the underwater environment. Moreover, the proposed control law adopts quaternions to represent attitude errors, and thus avoids representation singularities that occur when using instead Euler angles description of the orientation. The adaptive controller has been successfully implemented and experimentally validated on omnidirectional intelligent navigator (ODIN), an autonomous underwater vehicle that has been designed and built at the University of Hawaii. The experimental results demonstrate the good performance of the proposed controller within the constraints of the sensory system.

Adaptive control of an autonomous underwater vehicle: experimental results on ODIN

Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault

Describes a neural network system which executes identification of robot dynamics and controller adaptation in parallel with robot control. The system consists of two parts: real-world part and imaginary-world part. The real-world part is a feedback control system for the actual robot. In the imaginary-world part, the model of robot and the controller are adjusted continuously in order to deal with the change of dynamic property caused by disturbance and so on. The system is designed to be suitable for a computer system with parallel processing ability. In the paper, adaptability of the controller system is investigated by heading keeping and path following experiments on the condition that unknown disturbances are given to the robot.

Neural network system for online controller adaptation and its application to underwater robot

A new method to analyze the dynamics of underwater robot manipulators is proposed in this paper. In the proposed method, hydrodynamic terms such as added mass, drag and buoyancy in dynamics of underwater robots are obtained by iterative learning control and time-scale transformation. The advantage of the proposed method is not to use parameter estimation of the dynamics. In this paper, we explain that the proposed method can be applied to hardware design, motion control and motion planning of underwater robots. Moreover, the experimental results using a 1-DOF and a 3-DOF manipulator demonstrate the effectiveness of the proposed method.

Analysis on dynamics of underwater robot manipulators based on iterative learning control and time-scale transformation

A learning control approach to an underwater robotic vehicle system using neural networks is described. The objective is to have a robust control system with respect to changes in the vehicle dynamics and parameters. It has been observed that dominant vehicle dynamics vary with the vehicle velocity and that the effect of thruster dynamics becomes significant at low velocity in the vehicle control system. When it is necessary to operate the vehicle at low velocity (e.g., stationkeeping and hovering), the effect of thruster dynamics must be considered in the vehicle control system design. Results of computer simulation for pitch and altitude vehicle motion show the effectiveness of the proposed control system. >

Learning control of underwater robotic vehicles

The Deep Submergence Laboratory at the Woods Hole Oceanographic Institution is currently developing supervisory control system methodologies for underwater vehicles and manipulators. These technologies are currently being applied to Several tethered systems under development at the laboratory, however much of the work is directly applicable to untethered systems as well. Specific areas under investigation include man-machine interface design, control theoretic aspects of vehicle and manipulator control, and navigation sensors. The design of the closed-loop control system is a primary issue for any underwater vehicle that is not controlled manually. Precise trajectory control of all vehicles movements is particularly difficult due to the nonlinear, uncertain nature of the dynamics of underwater systems. A new control system design methodology called sliding control has been shown to deal with these difficult problems very effectively. In this paper, methodology and results from simulation and actual tests in the water are reviewed.

J.-J. Slotine

Papers

Robust trajectory control of underwater vehicles