The underwater swimming manipulator (USM) is a snake-like, multi-articulated, underwater robot that is equipped with thrusters. One of the main purposes of the USM is to act like an underwater floating base manipulator. As such, it is essential to achieve good station-keeping and trajectory tracking performance for the USM by using the thrusters and by using the joints to attain the desired position and orientation of the head and tail of the USM. In this 'paper, we propose a sliding mode control (SMC) law, specifically the super-twisting algorithm with adaptive gains, for the trajectory tracking of the USM's centre of mass. A higher-order sliding mode observer is proposed for state estimation. Furthermore, we show the ultimate boundedness of the tracking errors. We demonstrate the applicability of the proposed control law and show that it leads to better performance than a linear PD-controller.

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The articulated intervention AUV (AIAUV) is an underwater swimming manipulator (USM) with intervention capabilities. Station-keeping and trajectory tracking are essential for the AIAUV to be able to move in confined spaces and to perform intervention tasks. In this paper we propose using the generalized super twisting algorithm, which is an extension of the regular super-twisting algorithm, for the trajectory tracking of the joint angles, position and orientation of the base of the AIAUV in 6DOF. Furthermore, we show the ultimate boundedness of the tracking errors. We also demonstrate the applicability of the proposed control law and compare the performance with the regular super-twisting algorithm with adaptive gains.

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The articulated intervention autonomous underwater vehicle (AIAUV) is an underwater swimming manipulator with intervention capabilities. Station-keeping and trajectory tracking are essential for the AIAUV to be able to move in confined spaces and to perform intervention tasks. In this paper, we propose using a generalized super-twisting algorithm (GSTA), which is an extension of the regular super-twisting algorithm, for the trajectory tracking of the position and orientation of the base of the AIAUV in 6DOF. We also propose using a higher-order sliding mode observer (HOSMO) for estimating the linear and angular velocities when velocity measurements are unavailable. Furthermore, we show the ultimate boundedness of the tracking errors for a control law using the GSTA and for a control law that combines the GSTA with a HOSMO. We also prove that the GSTA gives global uniform finite-time stability. Finally, we demonstrate the applicability of the proposed control laws with comprehensive simulation and experimental results.

In this paper a novel adaptive generalized supertwisting algorithm is proposed for a class of systems whose perturbations and uncertain control coefficients are time-and statedependent. The proposed approach uses dynamically adapted control gains, and it is proven that this ensures global finitetime convergence. A non-smooth strict Lyapunov function is used to obtain the conditions for the global finite-time stability. As a case study, it is also shown that the tracking errors of an articulated intervention AUV converge asymptotically to zero when the proposed adaptive generalized super-twisting algorithm is applied. A simulation study is performed that shows the effectiveness of the proposed algorithm.