The thruster is the crucial factor of an underwater vehicle system, because it is the lowest layer in the control loop of the system. However, an accurate and practical thruster model has not been utilized yet. In this paper, we propose an accurate and practical thrust modeling for underwater vehicles which considers the effects of ambient flow velocity. In this model, the axial flow velocity of the thruster, which is non-measurable, is represented by ambient flow velocity and propeller shaft velocity. Hence, contrary to previous models, the proposed model is practical since it uses only measurable states. Next, the whole thrust map is divided into three states according to the state of ambient flow and propeller shaft velocity, and one of the borders of the states is defined as Critical Advance Ratio (CAR). This classification explains the physical phenomenon of conventional experimental thrust maps. The proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within ± 2N force error. The comparison results show that the new model’s matching performance is significantly better than conventional models’.

In order to utilize hydrodynamic drag force on articulated robot moving in an underwater environment, a repetitive motion planning method subject to drag optimization is proposed. Repetitive trajectories, a preplanned periodic motion, are obtained from general polynomial trajectory planning method by connecting the end point of trajectory to start point by enforcing continuous motion. After drag torque is described in terms of joint angles and velocities, directional drag force in task space is derived, which is used as objective function to be optimized during a time period. The genetic algorithm (GA) is adopted to find optimal parameters composing the coefficients of polynomials. To verify the correctness of proposed method, periodic motion planning examples of simple two-link planner robot are presented in this paper.

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high performance control system of an AUV is needed to have the capacities of learning and adaptation to the variations of the AUV dynamics. In this paper, a linearly parameterized neural network is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle physical properties. The proposed controller guarantees uniform boundedness of the vehicle trajectory tracking errors and network weights estimation errors based on the Lyapunov stability theory, where the network reconstruction errors and disturbances in the vehicle dynamics are bounded by an unknown constant. Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

This paper presents a control scheme for obtaining high manoeuvrability of underwater robot manipulators mounted on a remotely operated vehicle (ROV). The motions of an underwater manipulator can affect the attitude and position of the ROV which should remain stationary in seabed operation. To compensate for the dynamic effect of the underwater manipulator on the ROV, the force-torque (F/T) information between the manipulator and the vehicle is used to regulate the states of the ROV. When an F/T sensor is practically unavailable, a disturbance observer can fill the role of the F/T sensor. This paper proposes a disturbance observer for estimating the interaction forces between the ROV and the manipulator. A two-link manipulator mounted on an ROV is considered and numerical simulations are performed to demonstrate the improvement on the manoeuvrability of the proposed controller.

The control of autonomous underwater robotic vehicles includes many challenges. The highly nonlinear, time-varying dynamic behavior of the robot continually changes the parameters of the system model. The uncertainties in hydrodynamic coefficient make system identification difficult. Disturbances acting on the robot, such as ocean currents, continually affect the system. The paper presents a non-regressor based adaptive control scheme for use on an underwater robotic vehicle. The control scheme is non-model based and, thus, does not require any knowledge of the system. The adaptive control algorithm estimates the control gains defined by the combinations of the bounded constants of system parameter matrices. The performance of the algorithm was evaluated through simulation. Additionally, experimental data using the ODIN vehicle was analyzed.

Hydrodynamics of the manipulator mounted on the vehicle are poorly known and time-varying. Furthermore, its motion is disturbed by the vehicle motion and current. This paper presents a non-regressor based adaptive control scheme with a disturbance observer for tracking the trajectory of underwater mobile platform-mounted manipulators. The presented control system does not require any information about the system. The disturbance observer regards the difference between the actual output and the output of the nominal model as an equivalent disturbance applied to the nominal model. The adaptive control law then estimates control gains defined by the combinations of the bounded constants of parameter matrices of the nominal model with disturbance error. The performance of the proposed control scheme was evaluated by a computer simulation with a two-link planar manipulator mounted on a one 1-DOF mobile platform. The effect of hydrodynamic forces acting on the manipulator was also considered.

A robust teleoperation controller design method for an underwater manipulator is proposed considering the master and the underwater slave separately. To achieve transparency and stability for a teleoperation of an underwater manipulator in unknown environments with time-varying uncertainties such as added mass, buoyancy, hydraulic drag and friction effect, an adaptive sliding mode control scheme is proposed for robust position tracking control of slave manipulator. To guarantee a force transparency in the master side, disturbance observer is used as a local controller for compensating a friction and coupled nonlinear dynamic effects of the master manipulator. Numerical simulations are performed to demonstrate the transparency and robustness of the proposed controller.