AUV near-wall-following control based on adaptive disturbance observer

Abstract When detecting the wall crack, interferences caused by wall effect will significantly increase the difficulty of controlling AUV. This paper focuses on observing and compensating for interferences in the design of the controller. The optimal weight distribution algorithm is proposed to obtain the desired real-time optimal heading, which ensures that the AUV can achieve autonomous following of the wall by adjusting the heading. The backstepping sliding mode controller based on a slow time-varying adaptive disturbance observer (ADO) is proposed, which can effectively observe and compensate for the interference during AUV near-wall following. Results show that the tracking error of the proposed controller can be greatly reduced compared with the PID controller, and the tracking error is significantly reduced compared with the conventional sliding mode controller. The proposed controller can converge fast with small overshoot and stable control effect. The ADO provides strong robustness against interference, such as wall effect. Thus, AUVs can stably perform near-wall-following tasks and capture clear videos.

[1]  Qin Zhang,et al.  Robust Magnetic Tracking of Subsea Cable by AUV in the Presence of Sensor Noise and Ocean Currents , 2018, IEEE Journal of Oceanic Engineering.

[2]  António Manuel Santos Pascoal,et al.  Dynamic positioning and way-point tracking of underactuated AUVs in the presence of ocean currents , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[3]  Xin Zhang,et al.  Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities , 2016 .

[4]  Stephen R. Turnock,et al.  Sliding mode heading control of an overactuated, hover‐capable autonomous underwater vehicle with experimental verification , 2018, J. Field Robotics.

[5]  Son-Cheol Yu,et al.  Second-order sliding-mode controller for autonomous underwater vehicle in the presence of unknown disturbances , 2014, Nonlinear Dynamics.

[6]  Chen Wei,et al.  Back-stepping control of underactuated AUV's depth based on nonlinear disturbance observer , 2015, 2015 34th Chinese Control Conference (CCC).

[7]  Hongde Qin,et al.  A novel adaptive second order sliding mode path following control for a portable AUV , 2018 .

[8]  Bidyadhar Subudhi,et al.  Nonlinear ℋ∞ state and output feedback control schemes for an autonomous underwater vehicle in the dive plane , 2018, Trans. Inst. Meas. Control.

[9]  Tamaki Ura,et al.  A conical laser light-sectioning method for navigation of Autonomous Underwater Vehicles for internal inspection of pipelines , 2009, OCEANS 2009-EUROPE.

[10]  Charalampos P. Bechlioulis,et al.  A robust sonar servo control scheme for wall-following using an autonomous underwater vehicle , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Liu Tieshen Forecasting Hydrodynamic Interaction when AUV Tracks the Pipeline , 2015 .

[12]  Kihun Kim,et al.  Analysis on the controlled nonlinear motion of a test bed AUV-SNUUV I , 2007 .

[13]  David Andreu,et al.  Thruster's dead-zones compensation for the actuation system of an underwater vehicle , 2015, 2015 European Control Conference (ECC).

[14]  Wei Li,et al.  Random Weighting Method for Multisensor Data Fusion , 2011, IEEE Sensors Journal.

[15]  Michael S. Triantafyllou,et al.  A self stabilizing underwater sub-surface inspection robot using hydrodynamic ground effect , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Yuanwei Jing,et al.  Adaptive disturbance observer based control for a class of nonlinear systems , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).