Tracking Control of an Articulated Intervention Autonomous Underwater Vehicle in 6DOF Using Generalized Super-twisting: Theory and Experiments

Abstract

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.

Adaptive dynamic programming‐based adaptive‐gain sliding mode tracking control for fixed‐wing unmanned aerial vehicle with disturbances

“…The error between u eq and its approximation ūeq can be very small via selecting an appropriate time constant 𝜏. 22,51 Assumption 3 (52,53). For lumped disturbance 𝚫 a , there exists ‖𝚫 a ‖ ≤ 𝛿 a ‖𝚽 1 (S)‖, where 𝛿 a is an unknown positive constant.…”

Continuous–Discrete Observation-Based Robust Tracking Control of Underwater Vehicles: Design, Stability Analysis, and Experiments

“…Although first-order sliding-mode control (SMC) laws are well known to ensure the stability of the equilibrium points of many systems subjected to disturbances and uncertainties, these techniques require infinite control bandwidths [1]. To resolve this critical problem, a continuous SMC based on a super-twisting algorithm was proposed [2]. In [3], a second-order SMC was developed by redesigning a super-twisting algorithm using saturation functions and backstepping-like techniques.…”

The generalized super‐twisting algorithm with adaptive gains

“…We have previously investigated the use of SMC for trajectory tracking for an AIAUV, since trajectory tracking is essential to allow an AIAUV to move in confined spaces and to perform intervention tasks (tasks where the robot interacts or is in contact with the environment). In References 21 and 22, we investigated the use of the STA with adaptive gains to control an AIAUV, and in References 23 and 24, we investigated the use of the original GSTA for this purpose. In Reference 25, we compared these two SMC algorithms through both simulations and experiments, and we observed that the STA with adaptive gains yielded better tracking results than the GSTA, even though the GSTA has better theoretical properties as it is proven to provide GFT stability for a larger class of systems.…”