Abstract：In order to compensate the centroid variability of the underwater welding vehicle (named HITUWV) caused by the movements of the 3-DOF (degree of freedom) Cartesian-type manipulator, a centroid-variability model based adaptive sliding mode controller is proposed. Firstly, a centroid variability model (CVM) of HITUWV is established to describe the centroid variability characteristics of HITUWV accurately. Then, a centroid-variability model based adaptive sliding-mode controller (CVM-ASMC) is designed, to realize a high-accuracy compensation of the centroid variability. Experimental results indicate that the proposed CVM-ASMC demonstrates a higher accuracy, a better stability and a lower power consumption than the conventional PID (proportional-integral-differential) controller and the model-based PID controller. As a result, CVM-ASMC can meet the control requirement of underwater operation with high stability and accuracy.
[1] Dong M J, Chou W S, Yao G D. A new navigation strategy for underwater robot in reactor pool combined propeller speed detection and dynamics analysis with sonar data correction[J]. Journal of Nuclear Science and Technology, 2018, 55(1):1-10. [2] Wang L L, Xie F X, Feng Y L, et al. Innovative methodology and database for underwater robot repair welding:A technical note[J]. ISIJ International, 2017, 57(1):203-205. [3] Kobayashi R, Kono N. Development of localization system using ultrasonic sensor for an underwater robot to survey narrow environment[J]. Journal of Nuclear Science and Technology, 2018, 55(7):733-745. [4] Li J K, Wu X Y, Xu T T, et al. A novel inspection robot for nuclear station steam generator secondary side with self-localization[J]. Robotics and Biomimetics, 2017, 4(1). DOI:10. 1186/s40638-017-0078-y. [5] Zhao F, Ma Y G, Sun Y S. Application and standardization trend of maintenance and inspection robot (MIR) in nuclear power station[C]//DEStech Transactions on Engineering and Technology Research. Lancaster, USA:Destech Publications, 2017:241-252. [6] Li Z D, Tao J G, Luo Y, et al. Hydrodynamic coefficients calculation of a complex-shaped underwater robot by simulation and prototype testing[C]//2nd International Conference on Advanced Robotics and Mechatronics. Piscataway, USA:IEEE, 2017:474-478. [7] Luo Y, Tao J G, Li Z D, et al. A novel underwater localization algorithm fusing AHRS with altimeters[C]//International Conference on Computer Science and Education. Piscataway, USA:IEEE, 2017:791-794. [8] Luo Y, Tao J G, Li Z D, et al. An accurate cavitation prediction thruster model based on Gaussian process regression[C]//IEEE International Conference on Robotics and Biomimetics. Piscataway, USA:IEEE, 2017:575-580. [9] Li Z D, Tao J G, Sun H, et al. Hydrodynamic calculation and analysis of a complex-shaped underwater robot based on computational fluid dynamics and prototype test[J]. Advances in Mechanical Engineering, 2017, 9(11). DOI:10.1177/1687814017734500. [10] Smallwood D A, Whitcomb L L. Model-based dynamic positioning of underwater robotic vehicles:Theory and experiment[J]. IEEE Journal of Oceanic Engineering, 2004, 29(1):169-186. [11] Budiyono A, Anam H, Setiawan J. Simulation and dynamic analysis of remotely operated vehicle (ROV) using PID controller for pitch movement[J]. Marine and Underwater Science and Technology, 2015, 1(1):23-31. [12] Kuo C L, Tsui C K, Pai N S, et al. A PID controller for the underwater robot station-keeping[C]//IEEE International Conference on Industrial Informatics. Piscataway, USA:IEEE, 2017:1242-1246. [13] Ariyachartphadungkit T, Vanijjirattikhan R, Hasegawa S. Implementation of PID posture controller for 6 DOF underwater robot[C]//International Conference on Electronics, Information, and Communications. Piscataway, USA:IEEE, 2016. DOI:10.1109/ELINFOCOM.2016.7563009. [14] Sarhadi P, Noei A R, Khosravi A. Model reference adaptive PID control with anti-windup compensator for an autonomous underwater vehicle[J]. Robotics and Autonomous Systems, 2016, 83:87-93. [15] Ishaque K, Abdullah S S, Ayob S M, et al. Single input fuzzy logic controller for unmanned underwater vehicle[J]. Journal of Intelligent & Robotic Systems, 2010, 59(1):87-100. [16] Carlucho I, de Paula M D, Wang S, et al. Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning[J]. Robotics and Autonomous Systems, 2018, 107:71-86. [17] Barbalata C, Dunnigan M W, Petillot Y. Position/force operational space control for underwater manipulation[J]. Robotics and Autonomous Systems, 2018, 100:150-159. [18] Tanakitkorn K, Wilson P A, Turnock S R, et al. Sliding mode heading control of an overactuated, hover-capable autonomous underwater vehicle with experimental verification[J]. Journal of Field Robotics, 2018, 35(3):396-415. [19] Nag A, Patel S S, Akbar S A. Fuzzy logic based depth control of an autonomous underwater vehicle[C]//IEEE International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing. Piscataway, USA:IEEE, 2013:117-123. [20] Cui R X, Chen L P, Yang C G, et al. Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities[J]. IEEE Transactions on Industrial Electronics, 2017, 64(8):6785-6795. [21] Fossen T I. Guidance and control of ocean vehicles[M]. Hoboken, USA:John Wiley & Sons Inc., 1994.
