Station-Keeping Control of Autonomous and Remotely-Operated Vehicles for Free Floating Manipulation

Abstract

This paper investigates the station-keeping control of autonomous and remotely-operated vehicles (ARVs) for free-floating manipulation under model uncertainties and external disturbances. A modified adaptive generalized super-twisting algorithm (AGSTA) enhanced by adaptive tracking differentiator (ATD) and reduced-order extended state observer (RESO) is proposed. The ATD is used to obtain the smooth reference signal and its derivative. The RESO is used to estimate and compensate for the model uncertainties and external disturbances in real-time, which enhances the robustness of the controller. The modified AGSTA ensures the fast convergence of the system states and maintains them in a predefined neighborhood of origin without overestimating control gains. Besides, the proposed new variable gain strategy completely avoids the control gains vibrating near the set minimum value. Thanks to the RESO, the proposed controller is model-free and can be easily implemented in practice. The stability of the closed-loop system is analyzed based on Lyapunov’s direct method in the time domain. Finally, the proposed control scheme is applied to the station-keeping control of Haidou-1 ARV, and the simulation results confirm the superiority of the proposed control scheme over the original AGSTA.

Model-Free High-Order Sliding Mode Controller for Station-Keeping of an Autonomous Underwater Vehicle in Manipulation Task: Simulations and Experimental Validation

“…In the stationkeeping phase, the external disturbances 𝑢 𝑜𝑐 = 0.75 It can be observed that the controller is able to keep the BlueROV2 in reference for all positions and orientations. Contrary to the simulation results reported in [20][21][22], there is no initial push on the positions when the disturbance is introduced and the tracking error It can be observed that the controller is able to keep the BlueROV2 in reference for all positions and orientations. Contrary to the simulation results reported in [20][21][22], there is no initial push on the positions when the disturbance is introduced and the tracking error remains zero for all simulations.…”

“…Contrary to the simulation results reported in [20][21][22], there is no initial push on the positions when the disturbance is introduced and the tracking error It can be observed that the controller is able to keep the BlueROV2 in reference for all positions and orientations. Contrary to the simulation results reported in [20][21][22], there is no initial push on the positions when the disturbance is introduced and the tracking error remains zero for all simulations. This demonstrates the robustness of the controller to overcome external disturbances quickly and effectively.…”

“…Once the vehicle reaches its position, it should maintain its position and orientation during the manipulation, overcoming unknown external disturbances caused either by the underwater environment or the manipulation task itself. This is known as station-keeping [20] and is critical to the success of a mission as it allows the operator or autonomous manipulator to have better control of the operation and reduces the risks of collisions that could damage either the vehicle, the manipulator, or the object.…”

“…The results showed an initial push for the vehicle of no more than 5 cm, which is compensated after a fraction of a second. Ding et al [20] investigated a modified adaptive generalized super-twisting algorithm supported by an adaptive tracking differentiator and reduced-order extended state observer for free-floating manipulation under model uncertainties and external disturbances. The authors performed numerical simulations to verify the feasibility and efficiency of their proposed control scheme when sensor noise, external disturbances, and 30% of parameter uncertainties were introduced.…”

Dexterity Based Viscous Resistance Optimization of a Deep-Sea Manipulator

“…With further study of ocean exploration, underwater intervention tasks have gradu ally become more complex, and the dexterity of underwater robots is being studied. Typ ical examples of this type of robot include Ocean One [1], TRIDENT I-AUV [2], Aquanau [3], and Haidou I [4] (Figure 1). This kind of robot breaks through the working mode o traditional underwater robots and has higher dexterity when performing underwater in tervention tasks.…”